Advances in Algorithms for Inference and Learning in Complex Probability Models
Computer vision is currently one of the most exciting areas of artificial intelligence research, largely because it has recently become possible to record, store and process large amounts of visual data. Impressive results have been obtained by applying discriminative techniques in an ad hoc fashion to large amounts of data, e.g., using support vector machines for detecting face patterns in images. However, it is even more exciting that researchers may be on the verge of introducing computer vision systems that perform realistic scene analysis, decomposing a video into its constituent objects, lighting conditions, motion patterns, and so on. In our view, two of the main challenges in computer vision are finding efficient models of the physics of visual scenes and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based generative probability models and their associated inference and learning algorithms for computer vision and scene analysis. We review exact techniques and various approximate, computationally efficient techniques, including iterative conditional modes, the expectation maximization algorithm, the mean field method, variational techniques, structured variational techniques, Gibbs sampling, the sum-product algorithm and “loopy” belief propagation. We describe how each technique can be applied to an illustrative example of inference and learning in models of multiple, occluding objects, and compare the performances of the techniques.