An algorithm of scenes description and analysis based on MRF

Author

Dongcheng Shi ; Lili Wang

Author_Institution

Coll. of Comput. Sci. & Eng., Changchun Univ. of Technol., Changchun, China

fYear

2011

fDate

19-22 Aug. 2011

Firstpage

1513

Lastpage

1516

Abstract

Markov random fields (MRF) can be used for a wide variety of vision problems. In this paper we will propose an algorithm of scenes feature description and analysis based on MRF. The theoretical framework is based on MRF and Bayesian estimation via the energy optimization. We analyze the texture feature. Using MRF to modeling on the image, then combine with texture information and use of Bayesian to obtain the energy function, through the iterative optimization algorithm to minimize the energy function. Experimental results will be provided to illustrate the performance of our method.

Keywords

Bayes methods; Markov processes; iterative methods; optimisation; Bayesian estimation; MRF; Markov random fields; energy function; energy optimization; iterative optimization algorithm; scene analysis algorithm; scene description algorithm; texture feature; texture information; Algorithm design and analysis; Bayesian methods; Computational modeling; Labeling; Markov random fields; Mathematical model; Bayesian; MRF; energy function; scene analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on

Conference_Location

Jilin

Print_ISBN

978-1-61284-719-1

Type

conf

DOI

10.1109/MEC.2011.6025760

Filename

6025760