Unsupervised clustering and feature discrimination with application to image database categorization

We introduce a new algorithm that performs clustering and feature weighting simultaneously and in an unsupervised manner. The clustering approach is based on a model of mutual synchronization of pulse-coupled oscillators. The feature set is divided into logical subsets of features, and a degree of relevance is dynamically assigned to each subset based on its partial degree of similarity. The performance of the proposed algorithm is illustrated by using it to categorize a collection of images using three sets of features