Discriminative codebook learning for Web image search

Abstract

Given the explosive growth of the Web images, image search plays an increasingly important role in our daily lives. The visual representation of image is the fundamental factor to the quality of content-based image search. Recently, bag-of-visual word model has been widely used for image representation and has demonstrated promising performance in many applications. In the bag-of-visual-word model, the codebook/visual vocabulary plays a crucial role. The conventional codebook, generated via unsupervised clustering approaches, does not embed the labeling information of images and therefore has less discriminative ability. Although some research has been conducted to construct codebooks with the labeling information considered, very few attempts have been made to exploit manifold geometry of the local feature space to improve codebook discriminative ability. In this paper, we propose a novel discriminative codebook learning method by introducing the subspace learning in codebook construction and leveraging its power to find a contextual local descriptor subspace to capture the discriminative information. The discriminative codebook construction and contextual subspace learning are formulated as an optimization problem and can be learned simultaneously. The effectiveness of the proposed method is evaluated through visual reranking experiments conducted on two real Web image search datasets.