Query Specific Fusion For Image Retrieval System

IJCSEC Front Page

Abstract:
This paper presents a new majority voting technique combines the two basic modalities of Web images textual and visual features of image in a re-annotation and search based framework. The proposed framework considers each web page as a voter to vote the relatedness of keyword to the web image, the proposed approach is not only pure combination between image low level feature and textual feature but it take into consideration the semantic meaning of each keyword that expected to enhance the retrieval accuracy. The proposed approach is not used only to enhance the retrieval accuracy of web images; but also able to annotate the unlabeled images.

Keywords: Web Image Retrieval, Ontology, Data Mining, Image Clustering, Extraction, Ranking

References:

  1. Y. Alemu, J. Koh, and M. Ikram, “Image Retrieval in Multimedia Databases: A Survey” Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009.
  2. R. Agrawal, T.S Imielin, A.T. Swami, “Mining Association Rules Between Sets of Items in Large Databases”, SIGMOD Rec, 1993, Vol. 22, No.2, pp. 207–216.
  3. Z. Chen, L. Wenyin, F. Zhang, H. Zhang, “Web Mining for Web Image Retrieval”, Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining archive, Vol. 52, No. 10, 2001, pp. 832-839.
  4. Z. Gong, Q. Liu, “Improving Keyword Based Web Image Search with Visual Feature Distribution and Term Expansion”, Journal Knowledge and Information Systems Vol. 21, No.1, 2009.
  5. R. He , N. Xiong, L.T. Yang, and J. H. Park, “Using Multi-Modal Semantic Association Rules to Fuse Keywords and Visual Features Automatically for Web Image Retrieval”, Information. Fusion , 2010.
  6. J. Hou, D. Zhang, Z. Chen1, L. Jiang, H. Zhang, and X. Qin, “Web Image Search by Automatic Image Annotation and Translation”, IWSSIP 17th International Conference on Systems, Signals and Image Processing, 2010.
  7. M.L. Kherfi, D. Ziou, A. Bernardi, “Image Retrieval from the World Wide Web: Issues, Techniques, and Systems”, ACM Computing Surveys Vol.36, No. 1, 2004, pp. 35–67.
  8. V. Mezaris, I. Kompatsiaris, and M. G. Strintzis, “An Ontology Approach to Object-Based Image Retrieval”, in Proc. IEEE ICIP, 2003.
  9. O. Murdoch, L. Coyle and S. Dobson, “Ontology-Based query Recommendation as A Support to Image Retrieval”. In Proceedings of the 19th Irish Conference in Artificial Intelligence and Cognitive Science. Cork, IE. 2008.
  10. A. Torralba, R. Fergus, W.T. Freeman, “80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition” Pattern Analysis and Machine Intelligence, IEEE, Vol. 30, No. 11, 2008, pp. 1958 – 1970
  11. R.C.F. Wong and C.H.C. Leung,”Automatic Semantic Annotation of Real-World Web Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 11, 2008, pp. 1933-1945.
  12. H. Wang, S. Liu, L.T. Chia, “Does Ontology Help in Image Retrieval ?: A Comparison Between Keyword, Text Ontology and Multi-Modality Ontology Approaches”, Proceedings of the 14th annual ACM International Conference on Multimedia, 2006, pp. 23-27.
  13. H. Xu, X. Zhou, L. Lin, Y. Xiang, and B. Shi, ”Automatic Web Image Annotation via Web-Scale Image Semantic Space Learning”, APWeb/WAIM 2009, LNCS 5446, pp. 211–222, 2009
  14. Y. Yang, Z. Huang, H. T. Shen, X. Zhou,” Mining Multi-Tag Association for Image Tagging”, Journal World Wide Web Archive, Vol. 14, No.2, 2011.