Gaussian mixture model for relevance feedback in image retrieval

Abstract

Relevance feedback (RF) has become a powerful technique in content-based image retrieval. Most RF methods assume that positive images follow the single Gaussian distribution, which is not sufficient to model the actual distribution of images due to the gap between the semantic concept and low-level features. In this paper, the Gaussian mixture model (GMM) is applied to represent the distribution of positive images in relevance feedback, and a novel method is proposed to estimate the parameters of the GMM. Both positive and negative examples are used to estimate the number of Gaussian components. Furthermore, due to the lack of training samples, unlabeled data are also incorporated to estimate the covariance matrices. Experimental results show that our GMM-based RF method outperforms that based on a single Gaussian model.