The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2011 vol.23)
pp: 360-372
Ja-Hwung Su , National Cheng Kung University, Taiwan
Wei-Jyun Huang , National Cheng Kung University, Taiwan
Philip S. Yu , University of Illinois at Chicago, Chicago
Vincent S. Tseng , National Cheng Kung University, Taiwan
ABSTRACT
Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.
INDEX TERMS
Content-based image retrieval, relevance feedback, query point movement, query expansion, navigation pattern mining.
CITATION
Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, Vincent S. Tseng, "Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 3, pp. 360-372, March 2011, doi:10.1109/TKDE.2010.124
REFERENCES
[1] M.D. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Steele, and P. Yanker, "Query by Image and Video Content: The QBIC System," Computer, vol. 28, no. 9, pp. 23-32, Sept. 1995.
[2] R. Fagin, "Combining Fuzzy Information from Multiple Systems," Proc. Symp. Principles of Database Systems (PODS), pp. 216-226, June 1996.
[3] R. Fagin, "Fuzzy Queries in Multimedia Database Systems," Proc. Symp. Principles of Database Systems (PODS), pp. 1-10, June 1998.
[4] J. French and X-Y. Jin, "An Empirical Investigation of the Scalability of a Multiple Viewpoint CBIR System," Proc. Int'l Conf. Image and Video Retrieval (CIVR), pp. 252-260, July 2004.
[5] D. Harman, "Relevance Feedback Revisited," Proc. 15th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 1-10, 1992.
[6] Y. Ishikawa, R. Subramanya, and C. Faloutsos, "MindReader: Querying Databases through Multiple Examples," Proc. 24th Int'l Conf. Very Large Data Bases (VLDB), pp. 218-227, 1998.
[7] X. Jin and J.C. French, "Improving Image Retrieval Effectiveness via Multiple Queries," Multimedia Tools and Applications, vol. 26, pp. 221-245, June 2005.
[8] D.H. Kim and C.W. Chung, "Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval," Proc. ACM SIGMOD, pp. 599-610, 2003.
[9] K. Porkaew, K. Chakrabarti, and S. Mehrotra, "Query Refinement for Multimedia Similarity Retrieval in MARS," Proc. ACM Int'l Multimedia Conf. (ACMMM), pp. 235-238, 1999.
[10] J. Liu, Z. Li, M. Li, H. Lu, and S. Ma, "Human Behaviour Consistent Relevance Feedback Model for Image Retrieval," Proc. 15th Int'l Conf. Multimedia, pp. 269-272, Sept. 2007.
[11] A. Pentalnd, R.W. Picard, and S. Sclaroff, "Photobook: Content-Based Manipulation of Image Databases," Int'l J. Computer Vision (IJCV), vol. 18, no. 3, pp. 233-254, June 1996.
[12] T. Qin, X.D. Zhang, T.Y. Liu, D.S. Wang, W.Y. Ma, and H.J. Zhang, "An Active Feedback Framework for Image Retrieval," Pattern Recognition Letters, vol. 29, pp. 637-646, Apr. 2008.
[13] J.J. Rocchio, "Relevance Feedback in Information Retrieval," The SMART Retrieval System—Experiments in Automatic Document Processing, pp. 313-323, Prentice Hall, 1971.
[14] Y. Rui, T. Huang, and S. Mehrotra, "Content-Based Image Retrieval with Relevance Feedback in MARS," Proc. IEEE Int'l Conf. Image Processing, pp. 815-818, Oct. 1997.
[15] Y. Rui, T. Huang, M. Ortega, and S. Mehrotra, "Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval," IEEE Trans. Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, Sept. 1998.
[16] J.R. Smith and S.F. Chang, "VisualSEEK: A Fully Automated Content-Based Image Query System," Proc. ACM Multimedia Conf., Nov. 1996.
[17] G. Salton and C. Buckley, "Improving Retrieval Performance by Relevance Feedback," J. Am. Soc. Information Science, vol. 41, no. 4, pp. 288-297, 1990.
[18] H.T. Shen, S. Jiang, K.L. Tan, Z. Huang, and X. Zhou, "Speed up Interactive Image Retrieval," VLDB J., vol. 18, no. 1, pp. 329-343, Jan. 2009.
[19] V.S. Tseng, J.H. Su, J.H. Huang, and C.J. Chen, "Integrated Mining of Visual Features, Speech Features and Frequent Patterns for Semantic Video Annotation," IEEE Trans. Multimedia, vol. 10, no. 2, pp. 260-267, Feb. 2008.
[20] V.S. Tseng, J.H. Su, B.W. Wang, and Y.M. Lin, "Web Image Annotation by Fusing Visual Features and Textual Information," Proc. 22nd ACM Symp. Applied Computing, Mar. 2007.
[21] K. Vu, K.A. Hua, and N. Jiang, "Improving Image Retrieval Effectiveness in Query-by-Example Environment," Proc. 2003 ACM Symp. Applied Computing, pp. 774-781, 2003.
[22] L. Wu, C. Faloutsos, K. Sycara, and T.R. Payne, "FALCON: Feedback Adaptive Loop for Content-Based Retrieval," Proc. 26th Int'l Conf. Very Large Data Bases (VLDB), pp. 297-306, 2000.
[23] P.Y. Yin, B. Bhanu, K.C. Chang, and A. Dong, "Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1536-1551, Oct. 2005.
[24] H. You, E. Chang, and B. Li, "NNEW: Nearest Neighbor Expansion by Weighting in Image Database Retrieval," Proc. IEEE Int'l Conf. Multimedia and Expo, pp. 245-248, Aug. 2001.
[25] X.S. Zhou and T.S. Huang, "Relevance Feedback for Image Retrieval: A Comprehensive Review," Multimedia Systems, vol. 8, no. 6, pp. 536-544, Apr. 2003.
37 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool