Scene interpretation: Unified modeling of visual context by particle-based belief propagation in hierarchical graphical model

Abstract

In this paper, we present a novel scene interpretation method by unified modeling of visual context using a hierarchical graphical model. Scene interpretation through object recognition is difficult clue to several sources of ambiguity (blur, clutter). We model the visual context of scene, object, and part to disambiguate them during recognition. A precisely designed hierarchical graphical model call represent the contexts ill a unified way. We also propose a new inference method, particle-based belief propagation, optimized to scene interpretation in this hierarchical graphical model. Such an inference method suits the high-level context of scene interpretation. In addition, our core inference is so general that it can be used in any complex inference problems. Experimental results validate the power of the proposed model of visual context to solve the ambiguities in scene interpretation.