Skip to main content
Open Access Publications from the University of California

Department of Statistics, UCLA

Department of Statistics Papers bannerUCLA

Unsupervised Learning of Probabilistic Object Models (POMs) for Object Classification, Segmentation and Recognition using Knowledge Propagation


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 segmentation/localization. This combined model can be used to train POM-edgelets, defined on edgelets, which gives a full POM with improved performance on classification. We give detailed experimental analysis on large datasets which show that the full POM is minvariant to scale and rotation of the object (for learning and inference) and performs inference rapidly. In addition, mwe show that we can apply POM’s to learn objects classes (i.e. when there are several objects and the identity of the object in each image is unknown). We emphasize that these models can match between different objects from the same category and hence enable object recognition.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View