Datasets for Computer Vision
Datasets for Computer Vision Research
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Fifteen Scene CategoriesThis is a dataset of fifteen natural scene categories that expands on the thirteen category dataset released by Fei-Fei Li. The two new categories areindustrial and store. Classification results for the fifteen categories are presented in the following paper:Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, accepted.Download (84 MB zip file) |
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3D Object Recognition Stereo DatasetThis dataset consists of 9 objects and 80 test images. The training images are stereo views for each of the 9 objects that are roughly equally spaced around the equatorial ring for each of them. The number of stereo views ranges from 7 to 12 for the different objects. The test images are monocular images of objects under varying amounts of clutter and occlusion and different lighting conditions. |
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3D Photography DatasetThis is a collection of ten multiview data sets captured in our lab. Each set consists of 24 images of a single rigid object, together with camera parameters and extracted apparent contours for each image. |
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Visual Hull DatasetsThis is a collection of visual hull datasets used in the following paper:Svetlana Lazebnik, Yasutaka Furukawa, and Jean Ponce. Projective Visual Hulls. Submitted to International Journal of Computer Vision, 2006.Browse and download |
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BirdsThis database contains 600 images (100 samples each) of six different classes of birds. The images are color JPEG, of variable resolution. The classes (each in its own directory) are as follows:
Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. A Maximum Entropy Framework for Part-Based Texture and Object Recognition. Proceedings of the IEEE International Conference on Computer Vision, Beijing, China, October 2005, vol. 1, pp. 832-838.Download: birds.zip (43 MB) |
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ButterfliesThis database contains 619 images of seven different classes of butterflies. The images are color JPEG, of variable resolution. The classes (each in its own directory) are as follows:
Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Semi-Local Affine Parts for Object Recognition. Proceedings of the British Machine Vision Conference, September 2004, vol. 2, pp. 959-968.Download: butterflies.zip (43 MB) |
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Object Recognition DatabaseThis database features modeling shots of eight objects and 51 cluttered test shots containing multiple objects. The images are color JPEGs, the resolutions are 1.2 Mpix (1280 x 960) and 3.7 Mpix (2200 x 1700). If you use this database in your own research, please cite the following paper:Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. 3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints. International Journal of Computer Vision, vol. 66, no. 3, March 2006, pp. 231-259.When reporting your results on this data, please refer to the comparative evaluation of different state of the art recognition methods contained in this article. Browse and download: Image directoryThe creation of this database was supported in part by the National Science Foundation under grants IIS-0308087 and IIS-0312438, the UIUC-CNRS Research Collaboration Agreement, the European FET-open project VIBES, the UIUC Campus Research Board, and the Beckman Institute. For questions, contact Fred Rothganger (rothgang -at- uiuc.edu). |
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Texture DatabaseThe texture database features 25 texture classes, 40 samples each. All images are in grayscale JPG format, 640x480 pixels. If you use this database in your own research, please cite the following paper:Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. A Sparse Texture Representation Using Local Affine Regions. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1265-1278, August 2005.Browse and download: Example images (four per texture)The creation of this database was supported in part by the National Science Foundation under grant IIS-0308087, the European project LAVA, the UIUC-CNRS Research Collaboration Agreement, the UIUC Campus Research Board, and the Beckman Institute. For questions, contact Svetlana Lazebnik (slazebni -at- uiuc.edu). |
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Video SequencesThis dataset is used for research on Euclidean upgrades based on minimal assumptions about the camera (e.g. square pixels).Browse and download: Example reconstructions |
Links to External Datasets
Texture
- The KTH-TIPS Image Database
- Brodatz Textures
- MeasTex --- a bunch of useful texture stuff
- Vision Texture (VisTex), MIT Media Lab
- CUReT (Columbia-Utrecht Reflectance and Texture Database)
- University of Oulu Texture Database
- Texture lab, Heriot Watt University, Edinburgh
Object Recognition
- ETH-80 Database --- 80 objects (8 basic categories), each object is represented by 41 views
- Columbia Object Image Library: COIL-20, COIL-100
- Dataset page maintained by the Visual Geometry Group, Oxford University
- Caltech Vision Group Archive (the same recognition data sets as on the Oxford page)
- UIUC Image Database for Car Detection
- Henry Schneiderman's Car Database
Image Libraries
- Digital Library Project, Berkeley
Statistics of Natural Images
- Hans van Hateren's Natural Stimuli Collection