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...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang Zhang
• Keywords: Basic Pom, Combined Pom, Computer Vision, CVPR 2008, Enable Object Recognition, Parameter Learning Algorithm, Probabilistic Object Models