MMM: a stochastic mechanism for image database queries

We present a mechanism called the Markov model mediator (MMM) to facilitate the effective retrieval for content-based image retrieval (CBIR). Different from the common methods in content-based image retrieval, our stochastic mechanism not only takes into consideration the low-level image content features, but also learns high-level concepts from a set of training data, such as access frequencies and access patterns of the images. The advantage of our proposed mechanism is that it exploits the structured description of visual contents as well as the relative affinity measurements among the images. Consequently, it provides the capability to bridge the gap between the low-level features and high-level concepts. Our experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results for user queries.

[1]  A. Guttman,et al.  A Dynamic Index Structure for Spatial Searching , 1984, SIGMOD 1984.

[2]  Shih-Fu Chang,et al.  MetaSEEk: a content-based metasearch engine for images , 1997, Electronic Imaging.

[3]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[4]  Rangasami L. Kashyap,et al.  A probabilistic-based mechanism for video database management systems , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[5]  Thomas S. Huang,et al.  Content-based image retrieval with relevance feedback in MARS , 1997, Proceedings of International Conference on Image Processing.

[6]  Ying Sun,et al.  A hierarchical approach to color image segmentation using homogeneity , 2000, IEEE Trans. Image Process..

[7]  Mei-Ling Shyu,et al.  Affinity-based probabilistic reasoning and document clustering on the WWW , 2000, Proceedings 24th Annual International Computer Software and Applications Conference. COMPSAC2000.

[8]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[9]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[10]  Romain Murenzi,et al.  Fast texture database retrieval using extended fractal features , 1997, Electronic Imaging.

[11]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[12]  Milind R. Naphade,et al.  A probabilistic framework for semantic video indexing, filtering, and retrieval , 2001, IEEE Trans. Multim..

[13]  Rangasami L. Kashyap,et al.  Indexing and searching structure for multimedia database systems , 1999, Electronic Imaging.

[14]  Gio Wiederhold,et al.  Mediators in the architecture of future information systems , 1992, Computer.

[15]  Mario A. Nascimento,et al.  On “shapes” of colors for content-based image retrieval , 2000, MULTIMEDIA '00.

[16]  Wayne H. Wolf,et al.  Hidden Markov model parsing of video programs , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Shi-Nine Yang,et al.  Color image retrieval based on hidden Markov models , 1997, IEEE Trans. Image Process..

[18]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[19]  Guojun Lu,et al.  Generic Fourier descriptor for shape-based image retrieval , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[20]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Electronic Imaging.