Conjunctive Patches Subspace Learning With Side Information for Collaborative Image Retrieval

Content-based image retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of relevance feedback schemes have been designed to bridge the semantic gap between low-level visual features and high-level semantic concepts for an image retrieval task. Various collaborative image retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., conjunctive patches subspace learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images, and the weakly similar information of unlabeled images together to learn a reliable subspace. We formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic datasets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.