AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION

In this study autoregressive model based on Bayesian approach is proposed for texture classification. Based on auto correlation coefficients, micro textures are identified and represented locally and then globally. The identified micro texture is represented as a local description, called texnum. Th...

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Main Authors: T. Karthikeyan, R. Krishnamoorthy
Format: Article
Language:English
Published: ICT Academy of Tamil Nadu 2012-08-01
Series:ICTACT Journal on Image and Video Processing
Subjects:
Online Access:http://ictactjournals.in/paper/IJIVP(Aug2012)_Vol3_Iss1_P7_485_491.pdf
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spelling doaj-52e1d4730b11482f845e6f9abb80deb82020-11-25T02:15:33ZengICT Academy of Tamil NaduICTACT Journal on Image and Video Processing0976-90990976-91022012-08-0131485491AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATIONT. Karthikeyan0R. Krishnamoorthy1Department of Computer Science, PSG College of Arts and Science, IndiaDepartment of Computer Science and Engineering, Anna University, Tiruchirappalli, IndiaIn this study autoregressive model based on Bayesian approach is proposed for texture classification. Based on auto correlation coefficients, micro textures are identified and represented locally and then globally. The identified micro texture is represented as a local description, called texnum. The global descripter, texspectnum, is obtained by simply observing the numbers of occurrences of the texnums that cover the entire image. The proposed representation scheme has been employed in both supervised and unsupervised classifications of textured images. The supervised classification is based on simple tests of hypotheses and the unsupervised classification is based on the modified K-means algorithm with minimum distance classifiers. The proposed method is demonstrated for classification of different types natural textured images. The average correct classification is better than the existing methods.http://ictactjournals.in/paper/IJIVP(Aug2012)_Vol3_Iss1_P7_485_491.pdfTexnumTexspectnumMicrotextureK-Means AlgorithmSupervised ClassificationUnsupervised Classification
collection DOAJ
language English
format Article
sources DOAJ
author T. Karthikeyan
R. Krishnamoorthy
spellingShingle T. Karthikeyan
R. Krishnamoorthy
AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION
ICTACT Journal on Image and Video Processing
Texnum
Texspectnum
Microtexture
K-Means Algorithm
Supervised Classification
Unsupervised Classification
author_facet T. Karthikeyan
R. Krishnamoorthy
author_sort T. Karthikeyan
title AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION
title_short AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION
title_full AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION
title_fullStr AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION
title_full_unstemmed AUTOREGRESSIVE MODEL BASED ON BAYESIAN APPROACH FOR TEXTURE REPRESENTATION
title_sort autoregressive model based on bayesian approach for texture representation
publisher ICT Academy of Tamil Nadu
series ICTACT Journal on Image and Video Processing
issn 0976-9099
0976-9102
publishDate 2012-08-01
description In this study autoregressive model based on Bayesian approach is proposed for texture classification. Based on auto correlation coefficients, micro textures are identified and represented locally and then globally. The identified micro texture is represented as a local description, called texnum. The global descripter, texspectnum, is obtained by simply observing the numbers of occurrences of the texnums that cover the entire image. The proposed representation scheme has been employed in both supervised and unsupervised classifications of textured images. The supervised classification is based on simple tests of hypotheses and the unsupervised classification is based on the modified K-means algorithm with minimum distance classifiers. The proposed method is demonstrated for classification of different types natural textured images. The average correct classification is better than the existing methods.
topic Texnum
Texspectnum
Microtexture
K-Means Algorithm
Supervised Classification
Unsupervised Classification
url http://ictactjournals.in/paper/IJIVP(Aug2012)_Vol3_Iss1_P7_485_491.pdf
work_keys_str_mv AT tkarthikeyan autoregressivemodelbasedonbayesianapproachfortexturerepresentation
AT rkrishnamoorthy autoregressivemodelbasedonbayesianapproachfortexturerepresentation
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