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Enhanced Caltech-256 Dataset for Object Recognition

The Caltech-256 dataset offers improved features over Caltech-101, with larger category sizes, non-aligned images, and reduced artifacts. Collection procedures involved image rating by sorters, resulting in a diverse set for object recognition tasks.

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Enhanced Caltech-256 Dataset for Object Recognition

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  1. Intro CALTECH 256 Greg Griffin, Alex Holub and Pietro Perona

  2. Overview • 256 Object Categories + Clutter • At least 80 images per category • 30608 images instead of 9144

  3. Caltech-101: Drawbacks • Smallest category size is 31 images: • Too easy? • left-right aligned • Rotation artifacts • Soon will saturate performance

  4. Caltech-256 : New Features • Smallest category size now 80 images • Harder • Not left-right aligned • No artifacts • Performance is halved • More categories • New and larger clutter category

  5. Category Sizes 101 clutter 256 clutter

  6. Collection Procedure • Similar to Caltech-101 (Li, Fergus, Perona) • Four sorters rate the images • good: a clear example • bad: confusing, occluded, cluttered, or artistic • not applicable: object category not present • 92,652 Images from Google and Picsearch • 32.1% were rated good and kept • Some images borrowed from 29 of the largest Caltech-101 categories (green)

  7. Taxonomy

  8. Taxonomy (zoom)

  9. Recall Diminishing returns from Google Images

  10. Test for Antonio Torralba Try to find: blimp, clutter, grasshopper, picnic-table, refrigerator, watermelon

  11. Test for Antonio Torralba watermelon refrigerator grasshopper blimp clutter picnic-table

  12. Localization? watermelon refrigerator grasshopper Caltech-101/256 are not recommended for object localization tests blimp clutter picnic-table

  13. Expect roughly half the 101 performance Benchmarks

  14. Clutter: 827 Background Images Stephen Shore, Uncommon Places

  15. Acknowledgements • Rob Fergus and Fei Fei Li, Pierre Moreels for code and procedures developed for the Caltech-101 image set • Marco Ranzato and Claudio Fanti for miscellaneous help • Sorters: Lis Fano, Nick Lo, Julie May, Weiyu Xu for making this image set possible with their hard work Download: http://vision.caltech.edu/Image_Datasets/Caltech256

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