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54 results for “detection feature”
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN
<p>This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' <em>(DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>).</em></p> <p>We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.</p> <p>The final models and code are made publicly available via Github: <a href="https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout">https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout</a>.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>) when using the data in this repository.</p> <p>The csv-file <em>FRCNN_Zoobot_SDSS_GZCS_detections.csv</em> has the following columns. Alternatively, the file <em>FRCNN_Zoobot_SDSS_GZCS_detections.gzip</em> contains the same data but stored as a parquet-file.</p> <table> <tbody><tr> <th>Column name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>specobjid</td> <td>SDSS spec object ID</td> </tr> <tr> <td>dr7objid</td> <td>SDSS DR7 object ID</td> </tr> <tr> <td>clump_id</td> <td>Clump index</td> </tr> <tr> <td>clump_label_id</td> <td>Clump label ID (1 or 2)</td> </tr> <tr> <td>clump_label_name</td> <td>Clump label name</td> </tr> <tr> <td>clump_score</td> <td>Detection score for the clump</td> </tr> <tr> <td>clump_centre_ra</td> <td>Clump centroid RA in degrees</td> </tr> <tr> <td>clump_centre_dec</td> <td>Clump centroid dec in degrees</td> </tr> <tr> <td>clump_flux_u</td> <td>Clump u-band flux in Jy</td> </tr> <tr> <td>clump_flux_g</td> <td>Clump g-band flux in Jy</td> </tr> <tr> <td>clump_flux_r</td> <td>Clump r-band flux in Jy</td> </tr> <tr> <td>clump_flux_i</td> <td>Clump i-band flux in Jy</td> </tr> <tr> <td>clump_flux_z</td> <td>Clump z-band flux in Jy</td> </tr> <tr> <td>clump_flux_err_u</td> <td>Clump u-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_g</td> <td>Clump g-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_r</td> <td>Clump r-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_i</td> <td>Clump i-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_z</td> <td>Clump z-band flux error in Jy</td> </tr> <tr> <td>clump_mag_u</td> <td>Clump u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_g</td> <td>Clump g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_r</td> <td>Clump r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_i</td> <td>Clump i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_z</td> <td>Clump z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_ext_mag_u</td> <td>Clump u-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_g</td> <td>Clump g-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_r</td> <td>Clump r-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_i</td> <td>Clump i-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_z</td> <td>Clump z-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u</td> <td>Clump corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_g</td> <td>Clump corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_r</td> <td>Clump corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_i</td> <td>Clump corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_z</td> <td>Clump corrected z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u_g</td> <td>Clump colour (u-g)</td> </tr> <tr> <td>clump_mag_corr_g_r</td> <td>Clump colour (g-r)</td> </tr> <tr> <td>clump_mag_corr_r_i</td> <td>Clump colour (r-i)</td> </tr> <tr> <td>clump_mag_corr_i_z</td> <td>Clump colour (i-z)</td> </tr> <tr> <td>clump_flux_ratio</td> <td>Est. clump/galaxy near-UV flux ratio (u-band)</td> </tr> <tr> <td>is_clump_3pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >3%</td> </tr> <tr> <td>is_clump_8pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >8%</td> </tr> <tr> <td>galaxy_ra</td> <td>Host galaxy RA in degrees</td> </tr> <tr> <td>galaxy_dec</td> <td>Host galaxy dec in degrees</td> </tr> <tr> <td>galaxy_z</td> <td>Host galaxy redshift</td> </tr> <tr> <td>galaxy_mag_u</td> <td>Host galaxy u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_g</td> <td>Host galaxy g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_r</td> <td>Host galaxy r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_i</td> <td>Host galaxy i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_z</td> <td>Host galaxy z-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_u</td> <td>Host galaxy u-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_g</td> <td>Host galaxy g-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_r</td> <td>Host galaxy r-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_i</td> <td>Host galaxy i-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_z</td> <td>Host galaxy z-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_flux_u</td> <td>Host galaxy u-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_g</td> <td>Host galaxy g-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_r</td> <td>Host galaxy r-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_i</td> <td>Host galaxy i-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_z</td> <td>Host galaxy z-band flux in Jy</td> </tr> <tr> <td>galaxy_expAB_r</td> <td>Host galaxy axis ratio from SDSS</td> </tr> <tr> <td>galaxy_expRad_r</td> <td>Host galaxy exponential fit scale radius from SDSS</td> </tr> <tr> <td>galaxy_lmass</td> <td>Host galaxy log mass in MSun</td> </tr> <tr> <td>galaxy_lssfr</td> <td>Host galaxy log specific SFR</td> </tr> <tr> <td>galaxy_mag_corr_u</td> <td>Host galaxy corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_g</td> <td>Host galaxy corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_r</td> <td>Host galaxy corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_i</td> <td>Host galaxy corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_z</td> <td>Host galaxy corrected z-band magnitude (AB-mag)</td> </tr> </tbody> </table> <p> </p>
Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms
<p>This repository contains a dataset for the research of domain generation algorithms (DGAs) and machine learning. More precisely, it targets dictionary-based DGAs.</p> <p><em>Constantinos Patsakis, Fran Casino: "Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms", Journal of Information Security and Applications, 2021.</em></p> <p>Features ordered as in the shared dataset:</p> <ul> <li>Family: DGA that the domain belongs to</li> <li>SLD: SLD of the Domain</li> <li>L-LEN: The length of Domain</li> <li>L-DIG: The number of digits in Domain</li> <li>L-CON-MAX: The maximum number of consecutive consonants Domain</li> <li>R-CON-VOW: Number of consonants divided by L-LEN </li> <li>L-SYM: The number of special characters</li> <li>R-SYM-LEN: L-SYM divided by L-LEN</li> <li>R-Dom-3G: Ratio of benign grams in Dom-3G</li> <li>R-Dom-4G: Ratio of benign grams in Dom-4G</li> <li>R-Dom-5G: Ratio of benign grams in Dom-5G</li> <li>L-W2: Number of words with more than 2 characters in Domain</li> <li>L-W3: Number of words with more than 3 characters in Domain</li> <li>R-WS-LEN: Dom-WS divided by L-LEN</li> <li>R-WDS-LEN: Dom-WDS divided by L-LEN</li> <li>R-W2-LEN: Dom-W2 divided by L-LEN</li> <li>R-W3-LEN: Dom-W3 divided by L-LEN</li> <li>M2-Dom-Ws: 2-Chain Markov English grams applied to Dom-WS</li> <li>M2-Dom-WDS: 2-Chain Markov English grams applied Dom-WDS</li> <li>E-Dom-WS: Entropy of Dom-WS </li> <li>E-Dom-WDS: Entropy of Dom-WDS</li> <li>E-Dom-W2: Entropy of Dom-W2</li> <li>E-Dom-W3: Entropy of Dom-W3</li> </ul>
Dataset for Detection and Segmentation of the Radiographic Features of Pulmonary Edema
<p><strong>Objectives:</strong> This comprehensive dataset is well suited for training, evaluating, and using machine learning models to detect, segment, and analyze radiological features associated with pulmonary edema in chest X-ray images.</p> <p><strong>Description:</strong> This dataset consists of a collection of chest X-rays extracted from the <a href="https://physionet.org/content/mimic-cxr-jpg/2.0.0/" target="_blank" rel="noopener">MIMIC database</a>, carefully collected at the Beth Israel Deaconess Medical Center. In total, it comprises 1000 chest X-rays obtained from 741 patients with features suggestive of edema. These X-rays were carefully selected for manual annotation. The annotations are rich and detailed, covering specific radiological features commonly associated with pulmonary edema, including cephalization, Kerley lines, pleural effusions, bat wings, and infiltrates. The dataset includes a wide variety of radiological features, with a total of 4263 annotations (<em>Table 1</em>). Furthermore, each chest radiograph is thoughtfully assigned a severity category, categorizing it as "no edema", "vascular congestion", "interstitial edema", or "alveolar edema".</p> <p><strong>Annotation Method:</strong> The annotation process was meticulously performed by a highly qualified clinician with over 10 years of radiology experience, utilizing both frontal and lateral views for each chest X-ray study. Cephalization and Kerley lines were delineated using polylines, while other features were delineated using binary masks. This methodological approach was carefully chosen to provide a comprehensive data set that would ensure accuracy in subsequent analyses and label assignments. </p> <p>Notably, all features are represented as bounding boxes, meticulously defined by their respective upper-left (x1; y1) and lower-right (x2; y2) corners. In addition, selected features are provided with masks encoded in base 64 format. To facilitate seamless decoding, we provide a conversion script called "mask_converter.py" that allows the transformation of encoded masks into a versatile numpy array format. This feature improves the usability of the dataset for precise analysis and deep learning applications.</p> <p><strong>Datasets:</strong></p> <ol> <li><strong>SLY dataset:</strong> The dataset contains chest X-ray images labeled by clinicians, including both stacked frontal and lateral images. We obtained this dataset by annotating it on the <a href="https://supervisely.com/" target="_blank" rel="noopener">Supervisely platform</a>, and it is stored in JSON and PNG formats.</li> <li><strong>Source dataset:</strong> The dataset is a transformed version of the SLY dataset. In this dataset, all annotations are consolidated into a single spreadsheet, and only frontal view images are represented.</li> <li><strong>Processed dataset: </strong>The dataset focuses exclusively on the lung area for analysis, as other areas surrounding the lung typically contain extraneous information that clinicians do not use in their decision-making process.</li> <li><strong>COCO dataset:</strong> A collection of subsets prepared in the <a href="https://towardsdatascience.com/how-to-work-with-object-detection-datasets-in-coco-format-9bf4fb5848a4" target="_blank" rel="noopener">COCO format</a> and suitable for training and testing. It includes subsets for each feature and for all features evaluated in this study.</li> </ol> <div> <p><strong>Access to the Study:</strong> Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories:</p> <ul> <li><strong>Source code:</strong> <a href="https://github.com/ViacheslavDanilov/edema_quantification" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/edema_quantification</a></li> <li><strong>Dataset:</strong> <a href="https://doi.org/10.5281/zenodo.8383776" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8383776</a></li> <li><strong>Lung segmentation models:</strong> <a href="https://doi.org/10.5281/zenodo.8393555" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393555</a></li> <li><strong>Radiographic feature detection models:</strong> <a href="https://doi.org/10.5281/zenodo.8393565" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393565</a></li> </ul> <p> </p> <p><em><strong>Table 1.</strong> Summary of annotated radiological features and severity labels</em></p> <table> <tbody> <tr> <td> <p><strong>Radiological feature</strong></p> </td> <td> <p><strong>Number </strong><strong>of objects</strong></p> </td> <td> <p><strong>Severity </strong><strong>l</strong><strong>abel</strong></p> </td> <td> <p><strong>Number of cases</strong></p> </td> </tr> <tr> <td> <p>Cephalization</p> </td> <td> <p>1656</p> </td> <td> <p>No edema</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Kerley line</p> </td> <td> <p>609</p> </td> <td> <p>Vascular congestion</p> </td> <td> <p>74</p> </td> </tr> <tr> <td> <p>Pleural effusion</p> </td> <td> <p>317</p> </td> <td> <p>Interstitial edema</p> </td> <td> <p>51</p> </td> </tr> <tr> <td> <p>Bat wing</p> </td> <td> <p>1604</p> </td> <td> <p>Alveolar edema</p> </td> <td> <p>595</p> </td> </tr> <tr> <td> <p>Infiltrate</p> </td> <td> <p>77</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>TOTAL</p> </td> <td> <p>4263</p> </td> <td> <p>TOTAL</p> </td> <td> <p>741</p> </td> </tr> </tbody> </table> <p> </p> </div>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from VBSLHE Dataset
<p>This repository contains a diverse set of features extracted from the VBSLHE dataset (laparoscopic gynecology) . These features will be utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] in the next editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>). </p> <p>We used a snapshot of the dataset provided by the Medical University of Vienna and Toronto that can be downloaded using the instructions provided at <a href="https://download-dbis.dmi.unibas.ch/mvk/">https://download-dbis.dmi.unibas.ch/mvk/</a>. It comprises 75 video files. We divided each video into video shots with a maximum duration of 5 seconds.</p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <p>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} } </p> </blockquote> <p> </p> <p>This repository (v2) comprises the following files:</p> <ul> <li><em><strong>msb.tar.gz </strong></em> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione").</li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original VBSLHE videos available.</li> <li><em><strong>features-aladin.tar.gz†</strong></em><strong> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-laion.tar.gz†</strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz† </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip2video.tar.gz† </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the video segments. <strong> </strong></li> <li><em><strong>objects-frcnn-oiv4.tar.gz* </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]).</li> <li><em><strong>objects-mrcnn-lvis.tar.gz*</strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz*</strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong>†Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the VBSLHE dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used (see links above). Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CV</p>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from MVK Dataset
<p>This repository contains a diverse set of features extracted from the marine video (underwater) dataset (MVK) . These features were utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] during the latest editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>). </p> <p>We used a snapshot of the MVK dataset from 2023, that can be downloaded using the instructions provided at <a href="https://download-dbis.dmi.unibas.ch/mvk/">https://download-dbis.dmi.unibas.ch/mvk/</a>. It comprises 1,372 video files. We divided each video into 1 second segments. </p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <pre>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} }</pre> </blockquote> <p> </p> <p>This repository comprises the following files:</p> <ul> <li><strong><em>msb.tar.gz </em></strong> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione"). </li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original MVK videos available.</li> <li><strong><em>features-aladin.tar.gz<sup>†</sup></em> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip-laion.tar.gz<sup>†</sup></strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz<sup>†</sup> </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip2video.tar.gz<sup>†</sup> </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the 1s video segments. <strong> </strong></li> <li><em><strong>objects-frcnn-oiv4.tar.gz<sup>*</sup> </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]). </li> <li><em><strong>objects-mrcnn-lvis.tar.gz<sup>*</sup></strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz<sup>*</sup></strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong><sup>†</sup>Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the MVK dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used (see links above). Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CV</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli
<p>In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0<sup>o </sup>, 90<sup>o</sup> , 180<sup>o</sup> , 270<sup>o</sup> ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, C<sub>z</sub>, P1, P2, P<sub>z</sub>, PO3, PO4, PO7, PO8, PO<sub>z</sub>, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2<sup>nd</sup> group, the 180-degree stimuli the 3<sup>rd</sup> group and the 270-degree stimuli the 4<sup>th</sup> group. Thus, it is made possible to test the developed features in more datasets. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 2: Overlapped EEG segments
<p>In this study, the short time Fourier transformation (STFT) is used to examine the stability of the SSVEP. As the frequency resolution decreases with window size, overlapped EEG segments (Figure 2) that give sufficient frequency resolution are used. Coefficient of variation and variation speed detection features are proposed by using frequency spectrum of the segments. In Equation 1, x(t) sequence defines overlapped EEG segments in time domain, and in Equation 2 the x(f) sequence defines these segments in frequency domain and m is number of segments. </p> <p> </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 1: Time vs frequency analysis of 10 Hz SSVEP response
<p>The stability of the SSVEP signal was examined by using wavelet analysis (Wu and Yao 2008). Since there is a trade-off between time and frequency resolution in wavelet analysis, examining the stability of SSVEP with wavelet analysis is getting harder in systems where the visual stimulus frequencies are close to each other, as shown in Figure 1. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 5. SSVEP detection accuracies by using PSD, CV and VS features
<p>When the results in Figure 5 are analyzed, it is seen that CV and VS features provide detection results similar to PSD, which is a familiar feature. Considering that the chance level is 12.5% in these dataset, CV and VS can be used as discriminative features for SSVEP. Also on some subjects, like S3 on 1st and 4th datasets, and S11 on 1st, 2nd and 4th datasets, the proposed features have given clearly better detection results than PSD.</p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 3: (fi) sequences at different frequencies
<p>In Equation 4, f<sub>nbk</sub> defines neighboring frequency and L defines number of neighboring. ► sequences are shown graphically in Figure 3. </p> <p>The first developed parameter for the stability of SSVEP is the coefficient of variation (CV). The variation in <span class="math-tex">\( (fi)\)</span> sequence that is obtained at EEG component with SSVEP response is expected to be less than the other sequences. . Equation 5 shows the calculation of CV. <span class="math-tex">\( ( ) i f \)</span> standard deviation of (f<sub>i</sub>) sequence. ( ) i f is the averaged value of (f<sub>i</sub>) sequence. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection
<p>The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection. </p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Planetary Surface Features Change Detection Dataset
<p><strong>Summary</strong></p> <p>This dataset contains bi-temporal images pairs with associated labels of <em>change </em>or <em>no-change</em> describing whether there was a change in surface features between the two images acquired over the same location at two different times. This dataset contains images from four different instruments orbiting three different planets, each of which contains four representations of the bi-temporal image pair: composite grayscale, absolute difference, signed difference, and autoencoder bottleneck representations (described in detail in [1]). We also include the grayscale image tiles these representational datasets were created from. All datasets contain 100x100 images tiles that were cropped from larger images. We describe each below. </p> <p><strong>Contents</strong></p> <p><strong>hirise_rsl.zip</strong> : all subdirectories contain <em>change </em>and <em>no-change </em>examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after HiRISE image of recurring slope lineae on Mars<em>. </em>Subdirectory names are garni_XXXXXX_YYYYYY where Garni is the name of the crater on Mars shown in the images, XXXXXX is the HiRISE image ID of the before image, and YYYYYY is the HiRISE image ID of the after image. The "*_lcn" ending on some directories indicates that local contrast normalization was applied. The "*_gs_illum" and "*_gs_slope" directories contain composite grayscale representation with a third band that contains the difference between illumination (illum) and slope values at the same locations. Images with line endings _vflip, _hflip, _rot90, _rot180, and _rot270 were the result of vertical flips, horizontal flips, 90-deg rotations, 180-deg rotations, and 270-deg rotations of the image with the corresponding prefix.</p> <p><strong>ctx_impacts.zip</strong> : all subdirectories contain <em>change </em>and <em>no-change </em>examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after CTX image of meteorite impacts on Mars. The prefix in each image name corresponds to the image pair described in the Appendix in [1].</p> <p><strong>lroc_impacts.zip </strong>: all subdirectories contain <em>change </em>and <em>no-change </em>examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after LROC image of meteorite and spacecraft landing impacts on the Moon. Filenames correspond to pair names provided in the Appendix in [1].</p> <p><strong>planet_misc.zip</strong> : all subdirectories contain <em>change </em>and <em>no-change </em>examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after PlanetScope image of miscellaneous processes on Earth. Filenames correspond to pair names provided in the Appendix in [1].</p> <p><strong>*_before_after_grayscale.zip</strong> : before and after grayscale tiles used to create image representations in above directories (indicated with _before and _after suffix in filenames). Images that contain "_0_" in the filename have the label <em>no-change </em>and images with "_1_" in the filename have the label <em>change</em>.</p> <p>[1] Kerner et al. (2019) Deep Learning Methods Toward Generalized Change Detection on Planetary Surfaces. In review at <em>Journal of Selected Topics in Earth Observations and Remote Sensing</em>.</p> <p><strong>Attribution</strong></p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.2373798 as well as the paper below:</p> <p>Kerner, H. R., Wagstaff, K. L., Bue, B. D., Gray, P. C., Bell, J. F., & Amor, H. B. (2019). Toward generalized change detection on planetary surfaces with convolutional autoencoders and transfer learning. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, <em>12</em>(10), 3900-3918.</p>
Capturing Pertinent Symbolic Features for Enhanced Content Based Misinformation Detection
<p>Datasets for the paper <em>Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection</em>. The compressed folder contains <strong><em>annotated</em></strong> data carried out using the symbolic models presented in the paper.</p>
Articulatory features of plosive consonants for early detection of Parkinson's disease
<p>These datasets contain temporal and spectral features extracted from the Voice Onset Time (VOT) segments of /p/, /t/ and /k/ syllables for a audio database composed of 27 individuals diagnosed with PD and 27 healthy controls. Data are presented both in CSV and Excel formats.</p> <p>These datasets have been created and used in a full computer-aided diagnosis system. All information about the feature extraction process is presented in:</p> <p>David Montaña, Yolanda Campos-Roca, Carlos J. Pérez, A Diadochokinesis-based expert system considering articulatory features of plosive consonants for early detection of Parkinson’s disease, Computer Methods and Programs in Biomedicine, Volume 154, 2018, 89-97, ISSN: 0169-2607, DOI: 10.1016/j.cmpb.2017.11.010.</p> <p> </p> <p> </p>
Data Transformation for Clustering Utilization for Feature Detection in MS
<p>Dataset and roof-of-concept method used for proceeding titled "Data Transformation for Clustering Utilization<br> for Feature Detection in MS" of IWBBIO 22 conference.</p>
Optimizing the detection of biological signals through a semi-automated feature selection tool
<p>This repository contains the .xml files generated by the MZmine (Schmid et al., 2023) software version 2.53 during the preprocessing of the raw data (.raw) according to the recommendations of the 2007 Metabolomics Standards Initiative (Summer et al., 2007).</p> <p> </p> <p>Schmid R et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol. 2023 Apr;41(4):447-449.</p> <p>Sumner et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007 Sep;3(3):211-221. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.