Image Segmentation Integrating Generative and Discriminative Methods
WU Yue-e, Houqin Bian · 2009
In this paper we present a Bayesian framework for segmenting images into their constituent visual patterns. The segmentation algorithm optimizes the posterior probability and outputs a scene representation as a hierarchical graph representation, in a spirit similar to stochastic grammars in natural language. This computational framework integrates two popular inference approaches-generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on sequence of bottom-up tests/filters. The final results are validated in a Bayesian framework. Our experiments illustrate the advantages and importance of combining bottom-up and top-down models and of performing segmentation. The work can be used as a basis to design robust and effective computer vision systems which can be used, to assist the blind and visually impaired, for content based image retrieval and many other applications.