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Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition

10 years 5 days ago
Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this as a structure learning task and our strategy is to learn and combine basic POM's that make use of complementary image cues. Each POM has algorithms for inference and parameter learning, but: (i) the structure of each POM is unknown, and (ii) the inference and parameter learning algorithm for a POM may be impractical without additional information. We address these problems by a novel structure induction procedure which uses knowledge propagation to enable POM's to provide information to other POM's and "teach them" (which greatly reduced the amount of supervision required for training). In particular, we learn a POM-IP defined on Interest Points using weak supervision [1, 2] and use this to train a POMmask, defined on regional features, which yields a combined POM which performs segme...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang Zhang
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