Spatial Bayesian Nonparametrics for Natural Image Segmentation
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1 Spatial Bayesian Nonparametrics for Natural Image Segmentation Erik Sudderth Brown University Joint work with Michael Jordan University of California Soumya Ghosh Brown University
2 Parsing Visual Scenes sky skyscraper sky dome buildings trees temple bell
3 Region Classification with Markov Field Aspect Models Verbeek & Triggs, CVPR 2007 Local: 74% MRF: 78%
4 Human Image Segmentation
5 Berkeley Segmentation Database & Boundary Detection Benchmark
6 BNP Image Segmentation Segmentation as Partitioning! How many regions does this image contain?! What are the sizes of these regions? Why Bayesian Nonparametrics?! Huge variability in segmentations across images! Want multiple interpretations, ranked by probability
7 The Infinite Hype! Infinite Gaussian Mixture Models! Infinite Hidden Markov Models! Infinite Mixtures of Gaussian Process Experts! Infinite Latent Feature Models! Infinite Independent Components Analysis! Infinite Hidden Markov Trees! Infinite Markov Models! Infinite Switching Linear Dynamical Systems! Infinite Factorial Hidden Markov Models! Infinite Probabilistic Context Free Grammars! Infinite Hierarchical Hidden Markov Models! Infinite Partially Observable Markov Decision Processes!!
8 Some Hope: BNP Segmentation Model!! Dependent Pitman-Yor processes!! Spatial coupling via Gaussian processes Inference!! Stochastic search & expectation propagation Learning!! Conditional covariance calibration Results!! Multiple segmentations of natural images
9 Pitman-Yor Processes The Pitman-Yor process defines a distribution on infinite discrete measures, or partitions 0 1 Dirichlet process:
10 Pitman-Yor Stick-Breaking
11 Human Image Segmentations Labels for more than 29,000 segments in 2,688 images of natural scenes
12 Statistics of Human Segments How many objects are in this image? Object sizes follow a power law Many Small Objects Some Large Objects Labels for more than 29,000 segments in 2,688 images of natural scenes
13 Why Pitman-Yor? Generalizing the Dirichlet Process!! Distribution on partitions leads to a generalized Chinese restaurant process!! Special cases of interest in probability: Markov chains, Brownian motion,! Power Law Distributions DP Number of unique clusters in N observations Size of sorted cluster weight k PY Heaps Law: Zipf s Law: Jim Pitman Natural Language Statistics Goldwater, Griffiths, & Johnson, 2005 Teh, 2006 Marc Yor
14 Feature Extraction! Partition image into ~1,000 superpixels! Compute texture and color features: Texton Histograms (VQ 13-channel filter bank) Hue-Saturation-Value (HSV) Color Histograms! Around 100 bins for each histogram
15 Pitman-Yor Mixture Model PY segment size prior k 1 π k = v k (1 v l ) l=1 v k Beta(1 a, b + ka) Assign features to segments z i Mult(π) Observed features (color & texture) x c i Mult(θ c z i ) x s i Mult(θ s z i ) π z 1 z 2 z 3 z 4 x 1 x 2 x 3 x 4 Color: Texture: Visual segment appearance model
16 Dependent DP&PY Mixtures Some dependent prior with DP/PY like marginals Assign features to segments z i Mult(π i ) Observed features (color & texture) x c i Mult(θ c z i ) x s i Mult(θ s z i ) π 1 π 2 π 3 π 4 z 1 z 2 z 3 z 4 x 1 x 2 x 3 x 4 Color: Texture: Kernel/logistic/probit stick-breaking process, order-based DDP,! Visual segment appearance model
17 Example: Logistic of Gaussians! Pass set of Gaussian processes through softmax to get probabilities of independent segment assignments Fernandez & Green, 2002 Woolrich & Behrens, 2006 Figueiredo et. al., 2005, 2007 Blei & Lafferty, 2006! Nonparametric analogs have similar properties
18 Discrete Markov Random Fields Ising and Potts Models Previous Applications! Interactive foreground segmentation! Supervised training for known categories!but learning is challenging, and little success at unsupervised segmentation. GrabCut: Rother, Kolmogorov, & Blake 2004 Verbeek & Triggs, 2007
19 Phase Transitions in Action Potts samples, 10 states sorted by size: largest in blue, smallest in red
20 Product of Potts and DP? Orbanz & Buhmann 2006 Potts Potentials DP Bias:
21 Spatially Dependent Pitman-Yor! Cut random surfaces (samples from a GP) with thresholds (as in Level Set Methods) π! Assign each pixel to the first surface which exceeds threshold (as in Layered Models) z 1 z 2 z 3 z 4 x 1 x 2 Duan, Guindani, & Gelfand, Generalized Spatial DP, 2007 x 3 x 4
22 Spatially Dependent Pitman-Yor! Cut random surfaces (samples from a GP) with thresholds (as in Level Set Methods)! Assign each pixel to the first surface which exceeds threshold (as in Layered Models) Duan, Guindani, & Gelfand, Generalized Spatial DP, 2007
23 Spatially Dependent Pitman-Yor! Cut random surfaces (samples from a GP) with thresholds (as in Level Set Methods)! Assign each pixel to the first surface which exceeds threshold (as in Layered Models)! Retains Pitman-Yor marginals while jointly modeling rich spatial dependencies (as in Copula Models)
24 Spatially Dependent Pitman-Yor Non-Markov Gaussian Processes: PY prior: Segment size Normal CDF Feature Assignments
25 Samples from PY Spatial Prior Comparison: Potts Markov Random Field
26 Inference!! Stochastic search & expectation propagation Outline Model!! Dependent Pitman-Yor processes!! Spatial coupling via Gaussian processes Learning!! Conditional covariance calibration Results!! Multiple segmentations of natural images
27 Mean Field for Dependent PY Factorized Gaussian Posteriors K Sufficient Statistics Allows closed form update of via K
28 Robustness and Initialization Log-likelihood bounds versus iteration, for many random initializations of mean field variational inference on a single image.
29 Alternative: Inference by Search Consider hard assignments of superpixels to layers (partitions) Marginalize layer support functions via expectation propagation (EP): approximate but very accurate Integrate likelihood parameters analytically (conjugacy) No need for a finite, conservative model truncation!
30 Discrete Search Moves Stochastic proposals, accepted if and only if they improve our EP estimate of marginal likelihood:!! Merge: Combine a pair of regions into a single region!! Split: Break a single region into a pair of regions (for diversity, a few proposals)!! Shift: Sequentially move single superpixels to the most probable region!! Permute: Swap the position of two layers in the order Marginalization of continuous variables simplifies these moves!
31 Inference Across Initializations Mean Field Variational EP Stochastic Search Best Worst Best Worst
32 BSDS: Spatial PY Inference Spatial PY (MF) Spatial PY (EP)
33 Inference!! Stochastic search & expectation propagation Outline Model!! Dependent Pitman-Yor processes!! Spatial coupling via Gaussian processes Learning!! Conditional covariance calibration Results!! Multiple segmentations of natural images
34 Covariance Kernels! Thresholds determine segment size: Pitman-Yor! Covariance determines segment shape: Roughly Independent Image Cues:!! Color and texture histograms within each region: Model generatively via multinomial likelihood (Dirichlet prior)!! Pixel locations and intervening contour cues: Model conditionally via GP covariance function probability that features at locations are in the same segment Berkeley Pb (probability of boundary) detector
35 Learning from Human Segments!! Data unavailable to learn models of all the categories we re interested in: We want to discover new categories!!! Use logistic regression, and basis expansion of image cues, to learn binary are we in the same segment predictors:!! Generative: Distance only!! Conditional: Distance, intervening contours,!
36 From Probability to Correlation There is an injective mapping between covariance and the probability that two superpixels are in the same segment.
37 Low-Rank Covariance Projection!! The pseudo-covariance constructed by considering each superpixel pair independently may not be positive definite!! Projected gradient method finds low rank (factor analysis), unit diagonal covariance close to target estimates
38 Prediction of Test Partitions Heuristic versus Learned Image Partition Probabilities Learned Probability versus Rand index measure of partition overlap
39 Comparing Spatial PY Models Image PY Learned PY Heuristic
40 Inference!! Stochastic search & expectation propagation Outline Model!! Dependent Pitman-Yor processes!! Spatial coupling via Gaussian processes Learning!! Conditional covariance calibration Results!! Multiple segmentations of natural images
41 Other Segmentation Methods FH Graph Mean Shift NCuts gpb+ucm Spatial PY
42 Quantitative Comparisons Berkeley Segmentation LabelMe Scenes!! On BSDS, similar or better than all methods except gpb!! On LabelMe, performance of Spatial PY is better than gpb Room for Improvement:!! Implementation efficiency and search run-time!! Histogram likelihoods discard too much information!! Most probable segmentation does not minimize Bayes risk
43 Multiple Spatial PY Modes Most Probable
44 Multiple Spatial PY Modes Most Probable
45 Spatial PY Segmentations
46 Conclusions Successful BNP modeling requires!!! careful study of how model assumptions match data statistics & model comparisons!! reliable, consistent (general-purpose?) inference algorithms, carefully validated!! methods for learning hyperparameters from data, often with partial supervision
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