Abstract
We present a user-centric system for visualization and layout for content-based image retrieval and browsing. Image features (visual and/or semantic) are analyzed to display and group retrievals as thumbnails in a 2-D spatial layout which conveys mutual similarities. Moreover, a novel subspace feature weighting technique is proposed and used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization engine can adapt to the user’s (time-varying) notions of content, context and preferences in style of interactive navigation. Monte Carlo simulations with synthetic “user-layouts” as well as pilot user studies have demonstrated the ability of this framework to accurately model or “mimic” users by automatically generating layouts according to their preferences.
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References
M. Stricker, M. Orengo, “Similarity of Color Images”, Proc. SPIE Storage and Retrieval for Image and Video Databases, 1995
J. R. Smith, S. F. Chang, “Transform Features for Texture Classification and Discrimination in Large Image Database”, Proc. IEEE Intl. Conf. on Image Proc., 1994
S. X. Zhou, Y. Rui and T. S. Huang, “Water-filling algorithm: A novel way for image feature extraction based on edge maps”, in Proc. IEEE Intl. Conf. On Image Proc., Japan, 1999
S. Santini, R. Jain, “Similarity measures”, IEEE PAMI, vol. 21, no. 9, 1999
M. Popescu, P. Gader, “Image Content Retrieval From Image Databases Using Feature Integration by Choquet Integral”, in SPIE Conference Storage and Retrieval for Image and Video Databases VII, San Jose, CA, 1998
D. M. Squire, H. MÜller, and W. Müller, “Improving Response Time by Search Pruning in a Content-Based Image Retrieval System, Using Inverted File Techniques”, Proc. of IEEE workshop on CBAIVL, June 1999
D. Swets, J. Weng, “Hierarchical Discriminant Analysis for Image Retrieval”, IEEE PAMI, vol. 21, no. 5, 1999
Y. Rubner, “Perceptual metrics for image database navigation”, Ph.D. dissertation, Stanford University, 1999
W. S. Torgeson, Theory and methods of scaling, John Wiley & Sons, New York, NY, 1958
Jolliffe, I. T., Principal Component Analysis, Springer-Verlag, New-York, 1986
S. Santini, Ramesh Jain, “Integrated browsing and querying for image databases”, July–September Issue, IEEE Multimedia Magazine, pp. 26–39, 2000
B. Moghaddam et al; “Visualization and Layout for Personal Photo Libraries,” International Workshop on Content-Based Multimedia Indexing (CBMI’01), September, 2001
Q. Tian, B. Moghaddam, T. S. Huang, “Display Optimization for Image Browsing,” International Workshop on Multimedia Databases and Image Communication (MDIC’01), September, 2001
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© 2002 Springer-Verlag Berlin Heidelberg
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Tian, Q., Moghaddam, B., Huang, T.S. (2002). Visualization, Estimation and User-Modeling for Interactive Browsing of Image Libraries. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds) Image and Video Retrieval. CIVR 2002. Lecture Notes in Computer Science, vol 2383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45479-9_2
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DOI: https://doi.org/10.1007/3-540-45479-9_2
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