Abstract:

• Overview 256 Object Categories + Clutter At least 80 images per category 30608 images instead of 9144
• Caltech-101: Drawbacks Smallest category size is 31 images: Too easy? left-right aligned Rotation artifacts Soon will saturate performance

• 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

• Collection Procedure Similar to Caltech-101 (Li, Fergus, Perona)

Four sorters rate the images 1 good: a clear example 2 bad: confusing, occluded, cluttered, or artistic 3 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)

• 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

Please site as: Griffin, G. Holub, AD. Perona, P. The Caltech 256. Caltech Technical Report. The technical report will be available shortly.

• Year: 2006
• URL: http://www.vision.caltech.edu/Image_Datasets/Caltech256/
• License: No license specified, the work may be protected by copyright.

Reference:

• http://academictorrents.com/details/7de9936b060525b6fa7f5d8aabd316637d677622
• http://www.vision.caltech.edu/Image_Datasets/Caltech256/