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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Yang, Y., Zha, Z.J., Gao, Y., Zhu, X., Chua, T.S.: Exploiting web images for semantic video indexing via robust sample-specific loss. IEEE Trans. Multimedia 16(6), 1677–1689 (2014)
Hu, M., Yang, Y., Shen, F., Zhang, L., Shen, H.T., Li, X.: Robust web image annotation via exploring multi-facet and structural knowledge. IEEE Trans. Image Process. 26, 4871–4884 (2017)
Yang, Y., Yang, Y., Huang, Z., Shen, H.T., Nie, F.: Tag localization with spatial correlations and joint group sparsity. In: CVPR, pp. 881–888 (2011)
Nie, L., Wang, M., Zha, Z., Li, G., Chua, T.S.: Multimedia answering: enriching text QA with media information. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 695–704 (2011)
Nie, L., Wang, M., Zha, Z.J., Chua, T.S.: Oracle in image search: a content-based approach to performance prediction. ACM Trans. Inf. Syst. 30(2), 13:1–13:23 (2012)
Xu, X., Shen, F., Yang, Y., Shen, H.T., Li, X.: Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans. Image Processing 26(5), 2494–2507 (2017)
Xu, X., He, L., Shimada, A., Taniguchi, R., Lu, H.: Learning unified binary codes for cross-modal retrieval via latent semantic hashing. Neurocomputing 213, 191–203 (2016)
Yang, Y., Zhang, H., Zhang, M., Shen, F., Li, X.: Visual coding in a semantic hierarchy. In: MM, pp. 59–68 (2015)
Shih, T.K.: Distributed Multimedia Databases: Techniques and Applications. IGI Global, Hershey (2002)
Yang, Y., Luo, Y., Chen, W., Shen, F., Shao, J., Shen, H.T.: Zero-shot hashing via transferring supervised knowledge. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 1286–1295 (2016)
Yang, Y., Zhang, H., Zhang, M., Shen, F., Li, X.: Visual coding in a semantic hierarchy. In: Proceedings of the 23rd ACM International Conference on Multimedia, MM 2015, pp. 59–68 (2015)
Nie, L., Yan, S., Wang, M., Hong, R., Chua, T.S.: Harvesting visual concepts for image search with complex queries. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 59–68 (2012)
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR, pp. 1778–1785 (2009)
Jacob, L., Obozinski, G., Vert, J.P.: Group lasso with overlap and graph lasso. In: ICML, pp. 433–440 (2009)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 267–288 (1996)
Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections. Arizona State University (2009)
Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: CVPR, pp. 817–824 (2011)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: SCG, pp. 253–262. ACM (2004)
Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: NIPS, pp. 1509–1517 (2009)
Kang, W.C., Li, W.J., Zhou, Z.H.: Column sampling based discrete supervised hashing. In: AAAI, pp. 1230–1236 (2016)
Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: CVPR, pp. 2074–2081 (2012)
Lin, G., Shen, C., Shi, Q., van den Hengel, A., Suter, D.: Fast supervised hashing with decision trees for high-dimensional data. In: CVPR, pp. 1963–1970 (2014)
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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