Similarity measure learning for image retrieval using feature subspace analysis

Practical content-based image retrieval systems require efficient relevance feedback techniques. Researchers have proposed many relevance feedback methods using quadratic form distance metric as similarity measure and learning similarity matrix by feedback samples. Existing methods fail to find the optimal and reasonable solution of similarity measure due to the small number of positive and negative training samples. In this paper, a novel approach of learning the similarity measure using feature subspaces analysis (FSA) is proposed for content-based image retrieval. This approach solves the similarity measure-learning problem by FSA on training samples, which improves generalization capacity and reserves robustness furthest simultaneously. Experiments on a large database of 13,897 heterogeneous images demonstrated a remarkable improvement of retrieval precision.