Exploiting Concept Correlation with Attributes for Semantic Binary Representation Learning

Recent years have witnessed the unprecedented efforts of visual representation for enabling various efficient and effective multimedia applications. In this paper, we propose a novel visual representation framework, which generates efficient semantic hash codes for visual samples by substantially exploring concepts, semantic attributes as well as their inter-correlations. Specifically, we construct a conceptual space, where the semantic knowledge of concepts and attributes is embedded. Then, we develop an effective on-line feature coding scheme for visual objects by leveraging the inter-concept relationships through the intermediate representative power of attributes. The code process is formulated as an overlapping group lasso problem, which can be efficiently solved. Finally, we binarize the visual representation to generate efficient hash codes. Extensive experiments have illustrated the superiority of our proposed framework on visual retrieval task as compared to state-of-the-art methods.