pyramids of features for categorization greg griffin and will coulter (see lazebnik et al., cvpr...
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Pyramids of FeaturesFor Categorization
Greg Griffin and Will Coulter(see Lazebnik et al., CVPR 2006, too)
Intuition: Approximates optimal partial matching
U
Adapted from http://people.csail.mit.edu/kgrauman/slides/pyr_match_iccv2005.ppt
Intuition [cont’d]: Combine bags of features with spatial information
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category
Disadvantages
• Same objects in different quadrants
• Objects sliced by bins
Possible Solutions
• Flipping / rotating image
• Sliding / shuffling histogram bins
Possible Solutions [cont’d]
• Split histogram in powers of three
Implementation Overview
Image and Feature Extraction (SIFT on regular grid)
↓
Vocabulary Translation (200 words (k-means))
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Histogram Generation (flips, slides, arbitrary mixing)
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Matching (full or partial pyramid)
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Decision (best match, voting, SVM)
Sanity Check 1Graphical mini-confusion matrix (and rot. invariance)
Sanity Check 2
Scene Database
Scene Database
81.1% vs 67.4%
(100)
Caltech 101 64.6% vs. 33.1% (30 , 16)
Caltech 101
minaret windsor chair joshua tree okapi
cougar body beaver crocodile ant
97.6% , 86.7% 94.6% , 80.0% 87.9% , 40.0% 87.8% , 33.3%
27.6% , 6.7% 27.5% , 13.3% 25.0% , 13.3% 25.0% , 0.0%
(30 , 16)
Caltech 101
Work in Progress
• 256 Performance– 64 times more work scene database– 6.4 times more work than 101
• SVM– one-vs-all weighting issues– speed it up?– improve performance
• Improvements– Flip, Slide, Arbitrary
• Powers-of-3 histogram bins
Open Questions
• Performance of arbitrary match bins– Try random sampling?– Allow multiple best matches?
• Chess/pattern example
• Grid example
• Optimal kernel level weights
Implementation Details
• [block diagram]• Images (288^2,b&w,squished) and feature
extraction (-weak,-pca,+sift)• Vocab generation (200 words, 20,000-
small)• Histogram(fliplr,flipud,slide,arbitrary,bag of
features)• match (full&partial pyramids)• Decision (best match,voting,SVM)