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Unsupervised Learning of Categories from Sets of Partially Matching Image Features. Kristen Grauman Trevor Darrell MIT. Spectrum of supervision. Less. More. Costs of supervision. Sacrifice scalability: practical limit on number of classes, number of training examples per class - PowerPoint PPT Presentation
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Unsupervised Learning of Categories from Sets of Partially Matching Image Features
Kristen GraumanTrevor DarrellMIT
Spectrum of supervisionMoreLess
Costs of supervisionSacrifice scalability: practical limit on number of classes, number of training examples per classBiases possible:human labeling could hinder potentialperformance
Related workFeature/part selection given category [Weber et al. ECCV 2000, Fergus et al. CVPR 2003, Berg et al. CVPR 2005,]
Leveraging previously seen categories or images[Fei-Fei et al. ICCV 2003, Murphy et al. NIPS 2003, Holub et al. 2005,]
Unsupervised category learning with probabilistic Latent Semantic Analysis [Hofmann 1999][Fergus et al., Quelhas et al., Sivic et al., ICCV 2005]
GoalsAutomatically recover categories from an unlabeled collection of images, and form predictive classifiers to label new images
Tolerate clutter, occlusion, common transformationsAllow optional, variable amount of supervisionEfficiency
Sets of local features
Partially matching setsoptimal partial matching
Clustering with a partial matching
Clustering with a partial matching
Clustering with a partial matching
Clustering with a partial matching
Computing the partial matchingOptimal matchingGreedy matchingPyramid match for sets with features[Grauman and Darrell, ICCV 2005]
Review: Pyramid match
Review: Pyramid matchEfficient time for pyramids and matchingOrders of magnitude faster than optimal match in practiceAccurateProduces rankings that are highly correlated with optimal matchUseful as kernel in discriminative classifier: 50% accuracy on Caltech101 with 15 training examples per class (58% with 30)Bounded expected cost error[ICCV 2005, JMLR (to appear)]
Pyramid match graphBuild graph over image collection, with edges weighted by pyramid match similarity values
Optional semi-supervisionAdjust pyramid match graph when pair-wise constraints are available. should groupshould not group
Graph partitioningEfficiently identify initial clusters with spectral clustering and normalized cuts criterion of [Shi & Malik]
Limitation of partial match graph partitionBackground feature matches
Extracting correspondencesExtend pyramid match to return approximate feature correspondences
Extracting correspondencesExtend pyramid match to return approximate feature correspondences
Extracting correspondences
Limitation of partial match graph partitionBackground feature matches
Inferring feature maskscontribution to match
Inferring feature masks
Refining intra-cluster matches
Refining intra-cluster matchesweighted feature mask
Refining intra-cluster matches
Refining intra-cluster matches
Refining intra-cluster matchesweighted feature mask
Selecting category prototypes13245
Selecting category prototypes
Inferred feature masksHarris-Affine detector [Mikolajczyk and Schmid]SIFT descriptors [Lowe]
Unsupervised recovery of category prototypes40 runs with 400 randomly selected images
Top percentile of prototypesPrototype accuracy / categoryCaltech-4 data set
Semi-supervised category labelingRecover categories and SVM classifiers from 400 unlabeled imagesClassify 2788 unseen examples40 runs with random cluster/test set/supervision selections
Caltech-4 data setRecognition rate / classAmount of supervisory information(number of must-group pairs)
Recent work:Vocabulary-guided pyramid matchUniform binsExtracting correspondences can be slow, scores inaccurate in high dimensions with uniform binsA vocabulary-guided pyramid match tunes pyramid partitions to the feature distributionAccurate for d > 100
[See our recent CSAIL tech report]
ContributionsEfficient unsupervised / semi-supervised category learning from sets of local featuresAutomatic recovery of per-image feature masks without class labelsExtension to pyramid match for explicit correspondences
Future workEnforce geometry, contiguous spatial regions for matching feature maskExplore exemplar-based classifiersAutomatic selection of number of categoriesIterative cluster refinement / mask inferenceOptimizing semi-supervision with a user