An analytic distance metric for Gaussian mixture models with application in image retrieval

Abstract. In this paper we propose a new distance metric for probability den-sity functions (PDF). The main advantage of this metric is that unlike the popu-lar Kullback-Liebler (KL) divergence it can be computed in closed form when the PDFs are modeled as Gaussian Mixtures (GM). The application in mind for this metric is histogram based image retrieval. We experimentally show that in an image retrieval scenario the proposed metric provides as good results as the KL divergence at a fraction of the computational cost. This metric is also com-pared to a Bhattacharyya-based distance metric that can be computed in closed form for GMs and is found to produce better results.