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DHSR: A Novel Semantic Retrieval Approach for Ubiquitous Multimedia

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Abstract

Semantic features are critical intelligence information for mobile ubiquitous multimedia, how to manage and retrieve the semantic information has been an important issue. In this paper, a novel semantic retrieval approach named Data Hiding based Semantic Retrieval (DHSR) for ubiquitous multimedia is proposed. This approach consists of the following features: (1) Every multimedia document has to be semantically annotated by several users before saved into multimedia database. (2) Semantic information described by object ontology will be hidden in the multimedia document data. (3) Semantic information will not be lost even if the multimedia document is copied, cut or leave the database. Our work provides a search engine with convenient user interfaces. The experimental results show that DHSR can search the multimedia documents reflecting users’ query intent more effectively compared with some traditional approaches.

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References

  1. Tang, X., Liu, K., Cui, J., Wen, F., & Wang, X. (2012). IntentSearch: Capturing user intention for one-click internet image search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7), 1342–1353.

    Article  Google Scholar 

  2. Wong, R. C. F., & Leung, C. H. C. (2008). Automatic semantic annotation of real-world web images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), 1933–1944.

    Article  Google Scholar 

  3. Gijsenij, A., & Gevers, T. (2010). Color constancy using natural image statistics and scene semantics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), 687–698.

    Article  Google Scholar 

  4. Lei, W., Hoi, S. C. H., & Nenghai, Y. (2010). Semantics-preserving bag-of-words models and applications. IEEE Transactions on Image Processing, 19(7), 1908–1920.

    Article  MathSciNet  Google Scholar 

  5. Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1–2), 177–196.

    Article  MATH  Google Scholar 

  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(1), 993–1022.

    MATH  Google Scholar 

  7. Gao, Y., Fan, J., Luo, H., Xue, X., & Jain, R. (2006) Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers. In The 14th ACM international conference on multimedia, Santa Barbara, CA, USA (pp. 901–910). USA: ACM.

  8. Yang, S., Kim, S. K., & Ro, Y. M. (2007). Semantic home photo categorization. IEEE Transactions on Circuits and Systems for Video, 17(3), 324–335.

    Article  Google Scholar 

  9. Adankon, M., & Cheriet, M. (2009). Model selection for the LS-SVM: Application to handwriting recognition. Pattern Recognition, 42(12), 3264–3270.

    Article  MATH  Google Scholar 

  10. Carneiro, G., Chan, A., Moreno, P., & Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 394–410.

    Article  Google Scholar 

  11. Rasiwasia, N., Moreno, P. J., & Vasconcelos, N. (2007). Bridging the gap: Query by semantic example. IEEE Transactions on Multimedia, 9(5), 923–938.

    Article  Google Scholar 

  12. Sabine, B., & Salvatore, T. (2008). Visual features with semantic combination using Bayesian network for a more effective image retrieval. In International Conference on Pattern Recognition, Tampa, Florida, USA (pp. 1–4). USA: IEEE Computer Society Press.

  13. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.

    Article  Google Scholar 

  14. Chen, Y., Wang, J. Z., & Krovetz, R. (2003). An unsupervised learning approach to content-based image retrieval. In IEEE proceedings of the international symposium on signal processing and its applications, Paris, France (pp. 197–200). USA: IEEE Computer Society Press.

  15. Monay, F., & Gatica-Perez, D. (2007). Modeling semantic aspects for cross-media image indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(10), 1802–1817.

    Article  Google Scholar 

  16. Yang, C. B., Dong, M., & Hua, J. (2006). Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning. In IEEE computer society conference on computer vision and pattern recognition, New York, USA (pp. 2057–2063). USA: IEEE Computer Society.

  17. Djordjevic, D., & Izquierdo, E. (2007). An object and user driven system for semantic-based image annotation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 17(3), 313–323.

    Google Scholar 

  18. Hoi, S. C. H., Rong, J., Zhu, J., & Lyu, M. R. (2008). Semi-supervised SVM batch mode active learning for image retrieval. IEEE conference on computer vision and pattern recognition, Anchorage, AK (pp. 1–7). USA: IEEE Computer Society.

  19. Li, J., & Wang, J. Z. (2003). Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1075–1088.

    Article  Google Scholar 

  20. Li, J., & Wang, J. Z. (2006). Real-time computerized annotation of pictures. In The 14th ACM international conference on multimedia, Santa Barbara, CA, USA (pp. 911–920) USA: ACM.

  21. Liu, Y., Zhang, D., Lu, G., & Ma, W. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262–282.

    Article  MATH  Google Scholar 

  22. Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods and applications. Knowledge Engineering Review, 11(2), 93–155.

    Article  Google Scholar 

  23. Albertoni, R., Papaleo, R., & Pitikakis, M. (2005). Ontology-based searching framework for digital shapes. In Lecture Notes in Computer Science (Vol. 3762, pp. 896–905).

  24. Attene, M., Robbiano, F., & Spagnuolo, M. (2007). Part-based annotation of virtual 3d shapes. In International conference on cyberworlds, Hannover, Germany (pp. 427–436). USA: IEEE Computer Society. Press.

  25. Attene, M., Robbiano, F., Spagnuolo, M., & Falcidieno, B. (2009). Semantic annotation of 3d surface meshes based on feature characterization. In Lecture Notes in Computer Science (Vol. 4816, pp. 126–139).

  26. Gutiérrez, M., García-Rojas, A., & Thalmann, D. (2007). An ontology of virtual humans incorporating semantics into human shapes. The Visual Computer, 23(3), 207–218.

    Article  Google Scholar 

  27. Li, Z. J., Raskinm, V., & Ramani, K. (2008). Developing ontologies for engineering information retrieval. IASME Transactions Journal of Computing and Information Science in Engineering, 8(1), 1–13.

    MATH  Google Scholar 

  28. Wang, X. Y., Lv, T. Y., & Wang, S. S. (2008). An ontology and swrl based 3d model retrieval system. In Lecture Notes in Computer Science (Vol. 4993, pp. 335–344).

  29. Yang, D., Dong, M., & Miao, R. (2008). Development of a product configuration system with an ontology-based approach. Computer-Aided Design, 40(8), 863–878.

    Article  Google Scholar 

  30. Chua, T. S., Tang, J., Hong, R., Li, H., Luo, Z., & Zheng, Y. T. (2009). NUSWIDE: A real-world web image database from national university of Singapore. In Proceedings of the ACM international conference on image and video retrieval (pp. 1–9). New York, USA: ACM.

  31. Li, H. Wang, M., & Hua, X. S. (2009). MSRA-MM 2.0: A large-scale web multimedia dataset. In IEEE international conference on data mining, Miami, Florida, USA (pp. 164–169).

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Acknowledgments

Thanks for the invitation of Professor Laurence T. Yang in PiCom-2012. This work is supported by Natural Science Foundation of China (61202341, 61103203), China Postdoctoral Fund (2012M521552), Postdoctoral Fund of Hunan Province (2012RS4054) and Central South University.

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Correspondence to Kehua Guo.

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Guo, K., Ma, J. & Duan, G. DHSR: A Novel Semantic Retrieval Approach for Ubiquitous Multimedia. Wireless Pers Commun 76, 779–793 (2014). https://doi.org/10.1007/s11277-013-1327-1

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