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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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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1724895419546206208 |