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Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model
Author(s) -
Meng-Hsiun Tsai,
YungKuan Chan,
Jiun-Shiang Wang,
Guo Shuwei,
JiunnLin Wu
Publication year - 2009
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2009/410243
Subject(s) - artificial intelligence , computer vision , pattern recognition (psychology) , markov random field , image texture , random field , computer science , feature (linguistics) , distortion (music) , image retrieval , markov chain , feature detection (computer vision) , mathematics , image processing , image (mathematics) , image segmentation , statistics , machine learning , amplifier , computer network , linguistics , philosophy , bandwidth (computing)
The techniques of -means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model (GMRFM) feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations

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