Learning regularized, query-dependent bilinear similarities for large scale image retrieval

Abstract

An effective way to improve the quality of image retrieval is by employing a query-dependent similarity measure. However, implementing this in a large scale system is non-trivial because we want neither hurting the efficiency nor relying on too many training samples. In this paper, we introduce a query-dependent bilinear similarity measure to address the first issue. Based on our bilinear similarity model, query adaptation can be achieved by simply applying any existing efficient indexing/retrieval method to a transformed version (surrogate) of a query. To address the issue of limited training samples, we further propose a novel angular regularization constraint for learning the similarity measure. The learning is formulated as a Quadratic Programming (QP) problem and can be solved efficiently by a SMO-type algorithm. Experiments on two public datasets and our 1-million web-image dataset validate that our proposed method can consistently bring improvements and the whole solution is practical in large scale applications.