Caltech256 Image Dataset
Greg Griffin and Alex Holub and Pietro Perona

256_ObjectCategories.tar1.18GB
Type: Dataset
Tags:

Bibtex:
@article{,
title= {Caltech256 Image Dataset},
journal= {},
author= {Greg Griffin and Alex Holub and Pietro Perona},
year= {2006},
url= {http://www.vision.caltech.edu/Image_Datasets/Caltech256/},
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.},
keywords= {},
terms= {}
}


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