Adapting information theoretic clustering to binary images
We consider the problem of finding points of interest along local curves of binary images. Information theoretic vector quantization is a clustering algorithm that shifts cluster centers towards the modes of principal curves of a data set. Its runtime characteristics, however, do not allow for efficient processing of many data points. In this paper, we show how to solve this problem when dealing with data on a 2D lattice. Borrowing concepts from signal processing, we adapt information theoretic clustering to the quantization of binary images and gain significant speedup.