Pyramids of Features For Categorization Greg Griffin and Will Coulter (see Lazebnik et al., CVPR 2006, too)
Dec 21, 2015
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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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))
↓
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)