Combining Gaussian Mixture Models and Support Vector Machines for Relevance Feedback in Content Based Image Retrieval

Abstract

A relevance feedback (RF) approach for content based image retrieval (CBIR) is proposed, which combines Support Vector Machines (SVMs) with Gaussian Mixture (GM) models. Specifically, it constructs GM models of the image features distribution to describe the image content and trains an SVM classifier to distinguish between the relevant and irrelevant images according to the preferences of the user. The method is based on distance measures between probability density functions (pdfs), which can be computed in closed form for GM models. In particular, these distance measures are used to define a new SVM kernel function expressing the similarity between the corresponding images modeled as GMs. Using this kernel function and the user provided feedback examples, an SVM classifier is trained in each RF round, resulting in an updated ranking of the database images. Numerical experiments are presented that demonstrate the merits of the proposed relevance feedback methodology and the advantages of using GMs for image modeling in the RF framework.