Unsupervised Clustering of Images using their Joint Segmentation

We present a method for unsupervised content based classification of images. The idea is to first segment the images using centroid models common to all the images in the set and then through bringing an analogy between models/images and words/documents to apply algorithms from the field of unsupervised document classification to cluster the images. We choose our centroid models to be histograms of marginal distributions of wavelet coefficients on image subwindows. The models are used in our enhancement of [6] algorithm to jointly segment all the images in the input set. Finally we use the sequential Information Bottleneck algorithm [14] to cluster the images based on the result of the segmentation. The method is applied to nature views classification and painting categorization by drawing style. The method is shown to be superior to image classification algorithms that regard each image as a single model. We see our current work as opening a new perspective on high level unsupervised data analysis.