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Spatial Bayesian Nonparametrics for Natural Image Segmentation
Published on Jan 24, 20125574 Views
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Chapter list
Spatial Bayesian Nonparametrics for Natural Image Segmentation00:00
Parsing Visual Scenes00:05
Region Classification with Markov Field Aspect Models00:25
Human Image Segmentation01:41
Berkeley Segmentation Database & Boundary Detection Benchmark02:16
BNP Image Segmentation02:31
The Infinite Hype03:33
Some Hope: BNP Segmentation04:15
Pitman-Yor Processes04:39
Pitman-Yor Stick-Breaking05:22
Human Image Segmentations05:48
Statistics of Human Segments06:15
Why Pitman-Yor?07:11
An Aside: Toy Dataset Bias08:28
Feature Extraction09:19
Pitman-Yor Mixture Model09:53
Dependent DP&PY Mixtures10:02
Example: Logistic of Gaussians12:09
Discrete Markov Random Fields12:57
Phase Transitions in Action13:26
Product of Potts and DP?14:03
Spatially Dependent Pitman-Yor - 114:04
Spatially Dependent Pitman-Yor - 214:57
Spatially Dependent Pitman-Yor - 315:07
Spatially Dependent Pitman-Yor - 415:33
Samples from PY Spatial Prior16:17
Outline - 117:00
Mean Field for Dependent PY17:49
Robustness and Initialization19:13
Alternative: Inference by Search19:50
Discrete Search Moves21:30
Inference Across Initializations21:54
BSDS: Spatial PY Inference23:11
Outline - 223:37
Covariance Kernels23:40
Learning from Human Segments24:40
From Probability to Correlation25:24
Low-Rank Covariance Projection26:24
Prediction of Test Partitions27:09
Comparing Spatial PY Models27:50
Outline - 327:52
Other Segmentation Methods27:56
Quantitative Comparisons29:06
Multiple Spatial PY Modes - 130:12
Multiple Spatial PY Modes - 230:40
Spatial PY Segmentations31:04
Conclusions31:24