Intuitive Visualization for Online Image Retrieval

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We proposed a efficient and intuitive method to show the Online image retrieval result for searching information. The method incorporates visualization techniques in content-based image retrieval to show the hidden information in the result. We incorporated image browsing into online image retrieval. We give users a initial display based on PageRank, then use the users’ feedback to compute similarity function, then we compute the dissimilarity between images, get the position of images in the display space. If users are not satisfied with the display, they may feed back some more interested images to the system to improve the display. With the interactions provided by the system, users can browse a large number of images efficiently and find the exact images fast.

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549-553

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November 2010

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Mei Kobayashi, Koichi Takeda, Information Retrieval on the Web, ACM Computing Surveys, Volume 32,  Issue 2  (June 2000), pp.144-173.

DOI: 10.1145/358923.358934

Google Scholar

[2] Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 22 ,  Issue 12  (December 2000), pp.1349-1380.

DOI: 10.1109/34.895972

Google Scholar

[3] Jing Yang, Jianping Fan. Semantic Image Browser, Bridging Information Visualization with Automated Intelligent Image Analysis, IEEE Symposium on Visual Analytics Science and Technology, Oct. 31 2006-Nov. 2 2006, pp.191-198.

DOI: 10.1109/vast.2006.261425

Google Scholar

[4] Jianping Fan, Yuli Gao, Hangzai Luo, Hierarchical Classification for Automatic Image Annotation, ACM SIGIR. 2007, pp.111-118.

DOI: 10.1109/tmm.2007.900143

Google Scholar

[5] T. J. Jankun-Kelly, Kwan-Liu Ma. MoireGraphs, Radial Focus + Context Visualization and Interaction for Graphs with Visual Nodes, IEEE Symposium on Information Visualization, Oct. 2003, pp.59-66.

DOI: 10.1109/infvis.2003.1249009

Google Scholar

[6] Jack Kustanowitz, Ben Shneiderman., Meaningful presentations of photo libraries: rationale and applications of bi-level radial quantum layouts, ACM/IEEE-CS Joint Conference on Digital Libraries, June 07-11, 2005, pp.188-196.

DOI: 10.1145/1065385.1065431

Google Scholar

[7] David F. Huynh, Steven M. Drucker, Time quilt: scaling up zoomable photo browsers for large, unstructured photo collections, CHI Extended Abstracts. 2005, p.1937-(1940).

DOI: 10.1145/1056808.1057061

Google Scholar

[8] Jörg Walter, Daniel Wessling, Interactive Hyperbolic Image Browsing–Towards an Integrated Multimedia Navigator, ACM SIGKDD, 2006, pp.111-118.

Google Scholar

[9] GP Nguyen, M Worring., Similarity based visualization of image collections, The Seventh International Workshop on Audio–Visual Content and Information Visualization in Digital Libraries, 2005, p.22.

Google Scholar

[10] Jianping Fan, Yuli Gao, Hangzai Luo, Integrating Concept Ontology and Multitask Learning to Achieve More Effective Classifier Training for Multilevel Image Annotation, IEEE Transactions on Image Processing, Vol. 17, No. 3, MARCH, 2008, pp.407-426.

DOI: 10.1109/tip.2008.916999

Google Scholar

[11] Kerry Rodden, Wojciech Basalaj, Does Organisation by Similarity Assist Image Browsing?, ACM SIGCHI, 2001, pp.190-197.

DOI: 10.1145/365024.365097

Google Scholar

[12] Geoffrey Hinton, Sam Roweis, Stochastic Neighbor Embedding, Neural Information Processing Systems, (2002).

Google Scholar

[13] Joshua B. Tenenbaum. A Global Geometric Framework for Nonlinear Dimensionality Reduction. SCIENCE, (2000).

Google Scholar

[14] B. S. Manjunath, W. Y. Ma., Texture Features for Browsing and Retrieval of Image Data. IEEE Trans, on PAMI, (1996).

Google Scholar

[15] Koen E.A. van de Sande, Theo Gevers. A Comparison of Color Features for Visual Concept Classification. ACM CIVR. (2008).

Google Scholar

[16] Dengsheng Zhang, Guojun Lu, Review of shape representation and description techniques, Pattern Recognition, (2004).

Google Scholar

[17] Florica Mindru, Tinne Tuytelaars, Moment invariants for recognition under changing viewpoint and illumination, Computer Vision and Image Understanding, 2004, pp.3-27.

DOI: 10.1016/j.cviu.2003.10.011

Google Scholar

[18] Aharon Bar Hillel, Daphna Weinshall. Learning Distance Function by Coding Similarity, ACM ICML, (2007).

Google Scholar