Learning a mixture of sparse models by EM algorithm for object clustering

Abstract

Model-based object clustering is a very challenging unsupervised learning problem in computer vision, which involves both high dimensionality and hidden variables inference issues. In this paper, we will study object pattern clustering problem by using the Active basis model, which is a sparse representation model for object patterns. We fit a mixture of active basis models, which leads to an object clustering result, as well as the inference of all the hidden variables in the object clustering problem. This strategy not only gives us a way to represent each object by a sparse model, but also an elegant solution to the clustering problem with hidden variables, such as unknown locations, scales, and orientations of the objects appearing in the images. The experiment conducted on a small clustering dataset shows that learning a mixture of active basis models by EM-like algorithm for object clustering is very promising.