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.
T. Karthikeyan1 and R. Krishnamoorthy2
1PSG College of Arts and Science, India,2Anna University, Tiruchirappalli, India
Texnum, Texspectnum, Microtexture, K-Means Algorithm, Supervised Classification, Unsupervised Classification | Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 3 , Issue: 1 )
Date of Publication :
August 2012
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