Transcript powerpoint slides

Intro
CALTECH 256
Greg Griffin, Alex Holub and Pietro Perona
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: Ntrain  30
• 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
Category Sizes
101 clutter
256 clutter
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)
Taxonomy
Taxonomy (zoom)
Recall
Diminishing returns from Google Images
Test for Antonio Torralba
Try to find: blimp, clutter, grasshopper, picnic-table, refrigerator, watermelon
Test for Antonio Torralba
watermelon
refrigerator
grasshopper
picnic-table
blimp
clutter
Localization?
watermelon
refrigerator
grasshopper
Caltech-101/256 are not recommended for object localization tests
picnic-table
blimp
clutter
Benchmarks
Expect roughly
half the 101
performance
Clutter: 827 Background Images
Stephen Shore, Uncommon Places
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