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

• Smallest category size is 31 images:

• Too easy?

– left-right aligned

– Rotation artifacts

– Soon will saturate performance

Caltech-101: Drawbacks

€

N train ≤ 30

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 images1. good: a clear example2. bad: confusing, occluded, cluttered, or artistic3. 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

Try to find: blimp, clutter, grasshopper, picnic-table, refrigerator, watermelon

Test for Antonio Torralba

blimp clutter

watermelon refrigerator

picnic-table

grasshopper

Test for Antonio Torralba

blimp clutter

watermelon refrigerator

picnic-table

grasshopper

Localization?

Caltech-101/256 are not recommended for object localization tests

BenchmarksExpect roughly

half the 101performance

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