The Community for Technology Leaders
RSS Icon
Issue No.03 - March (2008 vol.20)
pp: 352-368
Relevance feedback (RF) is an iterative process which refines the retrievals by utilizing the user's feedback on previously retrieved results. Traditional RF techniques use solely the short-term learning experience and do not exploit the knowledge created during cross-sessions with multiple users. In this paper, we propose a novel RF framework which facilitates the combination of short-term and long-term learning processes by integrating the traditional methods with a new technique called the virtual feature. The feedback history with all the users is digested by the system and is represented in a very efficient form as a virtual feature of the images. As such, the dissimilarity measure can be adapted dynamically depending on the estimate of the semantic relevance derived from the virtual features. Also with a dynamic database, the user's subject concepts may transit from one to another. By monitoring the changes in retrieval performance, the proposed system can automatically adapt the concepts according to the new subject concepts. The experiments are conducted on a real image database. The results manifest that the proposed framework outperforms the one that adopts a traditional within-session RF technique.
Multimedia databases, Information Search and Retrieval, Query formulation, Relevance feedback
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei Dong, "Long-Term Cross-Session Relevance Feedback Using Virtual Features", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 3, pp. 352-368, March 2008, doi:10.1109/TKDE.2007.190697
[1] M. Flickner and H. Sawhney, “Query by Image and Video Content: The QBIC System,” Computer, vol. 28, no. 9, pp. 23-32, 1995.
[2] A. Pentland, R.W. Picard, and S. Sclaroff, “Photobook: Content-Based Manipulation of Image Databases,” Int'l J. Computer Vision, vol. 18, no. 3, pp. 233-254, 1996.
[3] Y. Rui, T.S. Huang, S. Mehrotra, and M. Ortega, “Automatic Matching Tool Selection Using Relevance Feedback in MARS,” Proc. Second Int'l Conf. Visual Information Systems, 1997.
[4] A. Yoshitaka and T. Ichikawa, “A Survey on Content-Based Retrieval for Multimedia Databases,” IEEE Trans. Knowledge and Data Eng., vol. 11, no. 1, pp. 81-93, Jan./Feb. 1999.
[5] A.W. Smeulders et al., “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
[6] Pattern Recognition, special issue on image database, J.C.M. Lee and A.K. Jain, eds., vol. 30, no. 4, 1997.
[7] X.S. Zhou, Y. Rui, and T.S. Huang, Exploration of Visual Data. Kluwer Academic Publishers, 2003.
[8] J.J. Rocchio, Jr., “Relevance Feedback in Information Retrieval,” The SMART System, G. Salton, ed., pp. 313-323, Prentice Hall, 1971.
[9] G. Ciocca and R. Schettini, “A Relevance Feedback Mechanism for Content-Based Image Retrieval,” Information Processing and Management, vol. 35, no. 6, pp. 605-632, 1999.
[10] Y. Rui et al., “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, 1998.
[11] J. Peng, B. Bhanu, and S. Qing, “Probabilistic Feature Relevance Learning for Content-Based Image Retrieval,” Computer Vision and Image Understanding, vol. 75, nos. 1-2, pp. 150-164, 1999.
[12] B. Bhanu, J. Peng, and S. Qing, “Learning Feature Relevance and Similarity Metrics in Image Databases,” Proc. IEEE Workshop Content-Based Access of Image and Video Libraries (CBAIVL '98), pp.14-18, 1998.
[13] C. Meilhac and C. Nastar, “Relevance Feedback and Category Search in Image Database,” Proc. Int'l Conf. Multimedia Computing and Systems (ICMCS '99), pp. 512-517, 1999.
[14] I. Cox et al., “The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments,” IEEE Trans. Image Processing, vol. 9, no. 1, pp. 20-37, 2000.
[15] S. Tong and E.Y. Chang, “Support Vector Machine Active Learning for Image Retrieval,” Proc. ACM Int'l Conf. Multimedia, pp. 107-118, 2001.
[16] K. Tieu and P. Viola, “Boosting Image Retrieval,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '00), pp. 228-235, 2000.
[17] N. Vasconcelos and A. Lippman, “Learning from User Feedback in Image Retrieval Systems,” Proc. Neural Information Processing System, 1999.
[18] A. Qamra, Y. Meng, and E.Y. Chang, “Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 379-391, March 2005.
[19] T.P. Minka and R.W. Picard, “Interactive Learning with a ‘Society of Models’,” Pattern Recognition, vol. 30, no. 4, pp. 565-581, 1997.
[20] B. Bhanu and A. Dong, “Exploitation of Meta Knowledge for Learning Visual Concepts,” Proc. IEEE Workshop Content-Based Access of Image and Video Libraries (CBAIVL '01), pp. 81-88, 2001.
[21] X. He, W.Y. Ma, O. King, M. Li, and H.J. Zhang, “Learning and Inferring a Semantic Space from User's Relevance Feedback for Image Retrieval,” Proc. ACM Int'l Conf. Multimedia, pp. 343-346, 2002.
[22] F. Jing, M. Li, H.J. Zhang, and B. Zhang, “Relevance Feedback in Region-Based Image Retrieval,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 5, pp. 672-681, 2004.
[23] A. Dong and B. Bhanu, “Active Concept Learning in Image Databases,” IEEE Trans. Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 35, no. 3, pp. 450-466, 2005.
[24] 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.
[25] C.-H. Hoi and M.R. Lyu, “A Novel Log-Based Relevance Feedback Technique in Content-Based Image Retrieval,” Proc. ACM Int'l Conf. Multimedia, pp. 24-31, 2004.
[26] Univ. of California, Riverside (UCR) Database, http://www.cris. ucr.eduDatabase.html, Aug. 2007.
[27] K.R. Castleman, Digital Image Processing. Prentice Hall, 1996.
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool