Image Parsing: Integrating Generative and Discriminative Methods

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Abstract: 0413214. Alan L. Yuille.The proposal will develop a computational framework for parsing images. Image parsing subsumes many standard computer vision tasks such as object detection and image segmentation. The approach is based on Bayesian inference using the Data Driven Markov Chain Monte Carlo (DDMCMC) algorithm. It involves modeling the diverse visual patterns that occur in natural images by hierarchical generative models. It also requires an algorithm, DDMCMC, that is capable of performing inference on these models. The design principle of DDMCMC is to use discriminative models to guide the search through the parameters of the generative models. Specific applications of image parsing include the development of computer vision systems to help the visually disabled by detecting and reading text, and detecting other salient objects such as faces. Hence we except this work to have broad impact in helping the visually disabled. Other applications include context based image retrieval and automatic security systems. The work will also help train 2-3 graduate students in Computer Science and Statistics.