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21 results for “image retrieval”
Touché22-Image-Retrieval-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef22/touche22-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2022.</p> <p>This version is lacking the touche22-image-search-archives.zip and touche22-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-22/</a></p>
Touché23-Image-Retrieval-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef23/touche23-web/image-retrieval-for-arguments.html">Image Retrieval for Arguments</a> task at Touché 2023.</p> <p>This version is lacking the touche23-image-search-archives.zip and touche23-image-search-screenshots.zip for space restrictions. Please get them from <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-23/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-23/</a></p> <p> </p>
Touché25-Image-Retrieval-and-Generation-for-Arguments
<p>Data for the <a href="https://touche.webis.de/clef25/touche25-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2025.</p> <p> </p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-search-25/version-2025-04-02/</a></p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Touché24-Image-Retrieval-and-Generation-for-Arguments
<div> <p>Data for the <a href="https://touche.webis.de/clef24/touche24-web/image-retrieval-for-arguments.html">Image Retrieval/Generation for Arguments</a> task at Touché 2024.</p> <p>Only the main.zip and nodes.zip are uploaded here due to space restrictions. Find the web page screenshots and web archives here: <a href="https://files.webis.de/corpora/corpora-webis/corpus-touche-image-retrieval-and-generation-24/">https://files.webis.de/corpora/corpora-webis/corpus-touche-image-retrieval-and-generation-24/</a></p> </div>
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents [HisIR19] Dataset
<p>This dataset contains the training and test set used in the ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents.</p> <p>This competition investigates the performance of large-scale retrieval of historical document images based on<br> writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing<br> a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval.</p> <p>The training data set encompasses images from (i) Letters A, where each writer contributed one or three images; (ii) Manuscripts, where each writer was represented by five consecutive images from a single book.<br> In total, it contains 300 writers contributing one page, 100 writers contributing three pages, and 120 writers contributing five pages resulting in 1200 images of 520 writers.</p> <p>The test data set contains 20 000 images: About 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages.</p> <p> </p> <p>If you use this dataset, please cite:</p> <p>V. Christlein, A. Nicolaou, M. Seuret, D. Stutzmann, A. Maier: "ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents", in 15th International Conference on Document Analysis and Recognition, 2019, Sydney, Australia</p> <p> </p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
Open the record for dataset details and reuse information.
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
The data to create figures in (Sulfur Dioxide Distribution at the Venusian Cloud-top Retrieved from Akatsuki UV Images).
<p>The data to create figures.</p>
Large-scale grid computing for content-based image retrieval
<p>The author presents an approach in which a large distributed processing Grid has been used to apply a range of content-based image retrieval methods to a substantial number of images. By massively distributing the required computational task across thousands of Grid nodes, we have achieved very high throughput at relatively low overheads.</p> <p> </p>
data set for Design of architectural environment integration of cyber-physical systems based on image segmentation and retrieval technology
<p>This data set is used to implement the project Design of architectural environment integration of cyber-physical systems based on image segmentation and retrieval technology</p>
Spectral data used in "Stratospheric-trace-gas-profile retrievals from balloon-borne limb imaging of mid-infrared emission spectra"
<p>The calibrated spectral data used in the trace gas retrievals by the Limb Imaging Fourier Transform Spectrometer Experiment (LIFE).</p>
ImageCLEF 2012 Image annotation and retrieval dataset (MIRFLICKR)
<p>DESCRIPTION<br> For this task, we use a subset of the MIRFLICKR (http://mirflickr.liacs.nl) collection. The entire collection contains 1 million images from the social photo sharing website Flickr and was formed by downloading up to a thousand photos per day that were deemed to be the most interesting according to Flickr. All photos in this collection were released by their users under a Creative Commons license, allowing them to be freely used for research purposes. Of the entire collection, 25 thousand images were manually annotated with a limited number of concepts and many of these annotations have been further refined and expanded over the lifetime of the ImageCLEF photo annotation task. This year we used crowd sourcing to annotate all of these 25 thousand images with the concepts.</p> <p>On this page we provide you with more information about the textual features, visual features and concept features we supply with each image in the collection we use for this year's task.</p> <p><br> TEXTUAL FEATURES<br> All images are accompanied by the following textual features:</p> <p>- Flickr user tags<br> These are the tags that the users assigned to the photos their uploaded to Flickr. The 'raw' tags are the original tags, while the 'clean' tags are those collapsed to lowercase and condensed to removed spaces.</p> <p>- EXIF metadata<br> If available, the EXIF metadata contains information about the camera that took the photo and the parameters used. The 'raw' exif is the original camera data, while the 'clean' exif reduces the verbosity.</p> <p>- User information and Creative Commons license information<br> This contains information about the user that took the photo and the license associated with it.</p> <p><br> VISUAL FEATURES<br> Over the previous years of the photo annotation task we noticed that often the same types of visual features are used by the participants, in particular features based on interest points and bag-of-words are popular. To assist you we have extracted several features for you that you may want to use, so you can focus on the concept detection instead. We additionally give you some pointers to easy to use toolkits that will help you extract other features or the same features but with different default settings.</p> <p>- SIFT, C-SIFT, RGB-SIFT, OPPONENT-SIFT<br> We used the ISIS Color Descriptors (http://www.colordescriptors.com) toolkit to extract these descriptors. This package provides you with many different types of features based on interest points, mostly using SIFT. It furthermore assists you with building codebooks for bag-of-words. The toolkit is available for Windows, Linux and Mac OS X.</p> <p>- SURF<br> We used the OpenSURF (http://www.chrisevansdev.com/computer-vision-opensurf.html) toolkit to extract this descriptor. The open source code is available in C++, C#, Java and many more languages.</p> <p>- TOP-SURF<br> We used the TOP-SURF (http://press.liacs.nl/researchdownloads/topsurf) toolkit to extract this descriptor, which represents images with SURF-based bag-of-words. The website provides codebooks of several different sizes that were created using a combination of images from the MIR-FLICKR collection and from the internet. The toolkit also offers the ability to create custom codebooks from your own image collection. The code is open source, written in C++ and available for Windows, Linux and Mac OS X.</p> <p>- GIST<br> We used the LabelMe (http://labelme.csail.mit.edu) toolkit to extract this descriptor. The MATLAB-based library offers a comprehensive set of tools for annotating images.</p> <p>For the interest point-based features above we used a Fast Hessian-based technique to detect the interest points in each image. This detector is built into the OpenSURF library. In comparison with the Hessian-Laplace technique built into the ColorDescriptors toolkit it detects fewer points, resulting in a considerably reduced memory footprint. We therefore also provide you with the interest point locations in each image that the Fast Hessian-based technique detected, so when you would like to recalculate some features you can use them as a starting point for the extraction. The ColorDescriptors toolkit for instance accepts these locations as a separate parameter. Please go to http://www.imageclef.org/2012/photo-flickr/descriptors for more information on the file format of the visual features and how you can extract them yourself if you want to change the default settings.</p> <p><br> CONCEPT FEATURES<br> We have solicited the help of workers on the Amazon Mechanical Turk platform to perform the concept annotation for us. To ensure a high standard of annotation we used the CrowdFlower platform that acts as a quality control layer by removing the judgments of workers that fail to annotate properly. We reused several concepts of last year's task and for most of these we annotated the remaining photos of the MIRFLICKR-25K collection that had not yet been used before in the previous task; for some concepts we reannotated all 25,000 images to boost their quality. For the new concepts we naturally had to annotate all of the images.</p> <p>- Concepts<br> For each concept we indicate in which images it is present. The 'raw' concepts contain the judgments of all annotators for each image, where a '1' means an annotator indicated the concept was present whereas a '0' means the concept was not present, while the 'clean' concepts only contain the images for which the majority of annotators indicated the concept was present. Some images in the raw data for which we reused last year's annotations only have one judgment for a concept, whereas the other images have between three and five judgments; the single judgment does not mean only one annotator looked at it, as it is the result of a majority vote amongst last year's annotators.</p> <p>- Annotations<br> For each image we indicate which concepts are present, so this is the reverse version of the data above. The 'raw' annotations contain the average agreement of the annotators on the presence of each concept, while the 'clean' annotations only include those for which there was a majority agreement amongst the annotators.</p> <p>You will notice that the annotations are not perfect. Especially when the concepts are more subjective or abstract, the annotators tend to disagree more with each other. The raw versions of the concept annotations should help you get an understanding of the exact judgments given by the annotators.</p>
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments (HisFrag20) Dataset
<p>This competition investigates the performance of large-scale retrieval of historical document fragments based on writer recognition. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists.<br> We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as fragment or writer retrieval. Therefore, we created a large dataset consisting of more than 120000 fragments.<br> The goal is then to find similar patches of the same page or manuscript. contains ~100 000 fragments using the Historical-IR19 as base dataset, they should all contain some text, however some fragments are quite small.</p> <p>Training-set: contains ~100 000 fragments using the Historical-IR19 as base dataset, they should all contain some text, however some fragments are quite small.</p> <p>Test-set: contains about 20 000 new fragments</p> <p>Naming-convention: WID_PID_FID.jpg , where WID=writer id, PID: page id, FID= fragment id</p> <p>For more information visit: <a href="https://lme.tf.fau.de/research/competitions/hisfragir20/">https://lme.tf.fau.de/research/competitions/hisfragir20/</a></p>
Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'
<p>1. "Retrieval_Database" file contains the pre-calculated retrieval database.</p> <p>2. "Algorithm_Input" file contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. "TrueProfiles" file contains the true profiles corresponding to the input brightness temperatures. </p> <p>4. "Algorithm_Output" file contains the output of the ensemble optimization retrieval algorithm.</p>
AWI CANON Sea ice image albedo retrieval calibration files
<p>The dataset contains calibration data, compiled for a Python based retrieval of a broadband albedo estimate from CANON EOS-1D Mark III cameras deployed as aerial imaging system over Arctic sea ice. </p> <p>It contains information on the relative spectral response function of the camera sensor (RSR_CANON), calibration coefficients to convert spectral albedo in the three RGB color bands into broadband albedo under different sky conditions (spec2broad_params) and calibration coefficients to convert RGB intensity into radiance dependent on exposure settings (Calib_coefficients_14mm_diff_settings).</p>
ELemental abundances of Planets and brown dwarfs Imaged around Stars (ELPIS): I. Potential Metal Enrichment of the Exoplanet AF Lep b and a Novel Retrieval Approach for Cloudy Self-luminous Atmospheres
<p>This dataset contains materials presented in the paper ``ELemental abundances of Planets and brown dwarfs Imaged around Stars (ELPIS): I. Potential Metal Enrichment of the Exoplanet AF Lep b and a Novel Retrieval Approach for Cloudy Self-luminous Atmospheres'' (Zhang et al. 2023) for publication by Astronomical Journal. Please cite this paper and the Zenodo repository if these data are used in your work.</p> <p>A readme file <strong>README_ELPIS_AFLep.dat</strong> describes the content of the entire dataset. Description of other files:</p> <ul> <li><strong>tull_spectrum_AF_Lep_A.zip</strong>: Tull spectrum of the host star AF Lep A (Section 2)</li> <li><strong>stellar_parameters_AF_Lep_A.zip</strong>: Stellar parameters of AF Lep A inferred by isochrones (Section 3)</li> <li><strong>orbit_analysis_AF_Lep_b.zip</strong>: Orbit analysis of the exoplanet AF Lep b (Section 4)</li> <li><strong>RCE_dlnT_dlnP_forward_models.zip</strong>: The radiave-convective equilibrium (dlnT/dlnP) profiles of several sets of forward models (shown in Figure 5 and Section 6.2)</li> <li><strong>mini_grid_emission_spectra.zip</strong>: A mini-grid of modeled emission spectroscopy (Figure 6 and Section 6.4)</li> <li><strong>fitted_TP_profiles.zip</strong>: Fitted T-P profiles from multiple retrieval runs (Section 7)</li> <li><strong>fitted_model_spectra.zip</strong>: Fitted model spectra from multiple retrieval runs (Section 7)</li> <li><strong>retrieval_parameter_posteriors.zip</strong>: Parameter posteriors from multiple retrieval runs (Section 7).</li> </ul>
Data Publication accompanying the paper "How FAIR can you get? Image Retrieval as a Use Case to calculate FAIR Metrics"
<pre>This dataset is the result of a benchmark run for a use-case-centric FAIR metric. The applied tech stack uses OAI-PMH and DataCite. The use case central to this benchmark is the retrieval of temporally and spatially annotated images. The zipped archives includes the data created during the first test run in June 2018. </pre>
Babel revisited: a taxonomy for ordinary images indexing in a bilingual retrieval context
<p>With the large volume of digital images now accessible on the World Wide Web, users<br> searching for images can be overwhelmed by many factors. Too many available images,<br> images indexed with an incomprehensible vocabulary or one that is too specialized to be<br> useful are but a few examples of issues leading to frustration. In addition, language barriers<br> still prevent Web users from retrieving the images they need.<br> This contribution presents the preliminary results of a study proposing to explore the<br> behaviours of image searchers from four different linguistic communities. The purpose of<br> this preliminary study is to examine queries formulated by image searchers to learn about<br> the terminology used and evaluate how this terminology can be eventually incorporated<br> into the development of a bilingual taxonomy for digital image indexing. Forty participants<br> from four different linguistic communities (English, French, Chinese and Russian native<br> speakers) were asked to write the queries they would use to retrieve ten images that were<br> shown to them consecutively. Then they were invited to fill out a questionnaire on their<br> behaviours as an image searcher on the Web.<br> The results of this research allowed the acquisition of knowledge of user terminology<br> standards and an assessment of how that terminology might be integrated in the develop-<br> ment of a bilingual taxonomy for improved indexing of ordinary digital images. Moreover,<br> since language barriers regularly prevent users from easily accessing information of all kinds, the<br> bilingual taxonomy will constitute a clear benefit for image searchers who are not overly<br> familiar with images indexed in English, which is still the dominant language of the Web.<br> </p>
Early Detection of Barrett's Esophagus Through Deep Image Retrieval
ClinicalTrials.gov study NCT04478929. IPD Sharing: NO. Countries: 1. Publications: 0.
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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)
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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.