A Face Tracker Trajectories Clustering Using Mutual Information

Abstract

In this paper we propose an algorithm for face tracker's trajectories clustering. Our approach is based on the mutual information of the images and more precisely its normalized version (NMI). We make use of 2 color channels from the HSV space (hue and saturation) in order to calculate a 4D joint histogram and therefore calculate the mutual information. In this paper we also develop an algorithm where we apply robust heuristics and make use of a tracker information in order to diminish dimensionality and augment accuracy of our results. It is a supervised clustering algorithm which is therefore used (fuzzy c-means) in order to gather same trajectories and same faces together.