An Efficient Fuzzy Hashing Model for Image Retrieval

Image retrieval models based on visual perception have traditionally placed more emphasis on object shape than other image features. Indexing and retrieval techniques are two major concerns for efficient management of multimedia databases. In this paper, a new shape-based image retrieval model coupled with grey theory, a fuzzy hashing table and relevance feedback based on association rules is proposed to search for images with similar object shape. A GM(1,N) model is used for the shape-based image retrieval. Next, a fuzzy hashing scheme is employed to solve the problem of hash misses. Furthermore, association rules mined from users' retrieval history are used to reveal users' image searching behavior. The proposed strategy is applied to the FISH dataset to demonstrate its effectiveness

[1]  Sugata Ghosal,et al.  An image retrieval system with automatic query modification , 2002, IEEE Trans. Multim..

[2]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[3]  原田 秀逸.  私の computer 環境 , 1998 .

[4]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[5]  Gerard Salton,et al.  Optimization of relevance feedback weights , 1995, SIGIR '95.

[6]  Chahab Nastar,et al.  Relevance feedback and category search in image databases , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

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

[8]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Thomas S. Huang,et al.  A novel relevance feedback technique in image retrieval , 1999, MULTIMEDIA '99.

[10]  Shaoping Ma,et al.  Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning , 2003, IEEE Trans. Image Process..

[11]  Ling Guan,et al.  Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture , 2002, IEEE Trans. Neural Networks.

[12]  Chabane Djeraba,et al.  Association and Content-Based Retrieval , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Shwu-Huey Yen,et al.  A study of shape-based image retrieval , 2004, 24th International Conference on Distributed Computing Systems Workshops, 2004. Proceedings..

[14]  Yasufumi Takama,et al.  Relevance feedback-based image retrieval interface incorporating region and feature saliency patterns as visualizable image similarity criteria , 2003, IEEE Trans. Ind. Electron..

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

[16]  R. Krishnapuram,et al.  A fuzzy approach to content-based image retrieval , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.