Abstract
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.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Project 61572108, Project 61632007, Project 61602089 and the Fundamental Research Funds for the Central Universities under Project ZYGX2014Z007, Project ZYGX2015J055.
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Wu, H., Yang, Y., Xu, X., Shen, F., Xie, N., Ji, Y. (2018). Exploiting Concept Correlation with Attributes for Semantic Binary Representation Learning. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_17
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DOI: https://doi.org/10.1007/978-981-10-8530-7_17
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