Color-Texture-Based Image Retrieval System Using Gaussian Markov Random Field Model

Meng-Hsiun Tsai, Yung-Kuan Chan, Jiun-Shiang Wang, Shu-Wei Guo and Jiunn-Lin Wu

Mathematical Problems in Engineering, 2009, vol. 2009, 1-17

Abstract:

The techniques of ð ¾ -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.

Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2009/410243.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2009/410243.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:410243

DOI: 10.1155/2009/410243

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().