A Hierarchical Part-Based Model for Visual Object Categorization

We propose a hierarchical generative model for coding the geometry and appearance of visual object categories. The model is a collection of loosely connected parts containing more rigid assemblies of subparts. It is optimized for domains where there are relatively large numbers of somewhat informative subparts, such as the features returned by local feature methods from computer vision. The model is learned quickly by an E-M procedure. Some experiments on real images show the its ability to fit complex natural object classes.