Abstract—This study addresses the statistical texture features as methods for texture classification. It compares its performance on two benchmark data sets: Brodatz and PID, which permit to gain better understanding how those methods deal with texture rotation and lighting changes. Moreover, few simple feature techniques are introduced in order to compare their performance with those already known (e.g. first order features, co-occurrence matrix, run length matrix, grey-tone difference matrix, local binary pattern). Finally, exploiting structures designed in methods like co-occurrence matrices as a feature vector is suggested. The gathered results show the correct classification ratio in range of 92-100%. However, worse performance is noticeable on data set with changing lighting conditions. Moreover, the experiments prove that the introduced simple techniques classify with similar accuracy as well as known methods. It is also interesting that exploiting the structures as feature vector proved to improve the classification results. Additionally, due to lower classification calculation complexity the feature vectors length have been diminished with the application of principal component analysis. This experiments showed that exploiting 95% of original information considerably reduces the feature vector length and does not influence the correct classification ratio of all tested methods.
The gathered results show the correct classification ratio inrange of 92-100%. However, worse performance is noticeableon data set with changing lighting conditions. Moreover, theexperiments prove that the introduced simple techniquesclassify with similar accuracy as well as known methods. It isalso interesting that exploiting the structures as feature vectorproved to improve the classification results.
Additionally, due to lower classification calculationcomplexity the feature vectors length have been diminishedwith the application of principal component analysis. Thisexperiments showed that exploiting 95% of originalinformation considerably reduces the feature vector length anddoes not influence the correct classification ratio of all testedmethods.
Index Terms—Image classification, image processing, texture operators.
Karolina Nurzynska, Mamoru Kubo, and Ken-ichioro Muramoto are with Kanazawa University of Technology, Kakuma, Kanazawa 920-1192, Japan (e-mail: karolina.olga.nurzynska@gmial.com, kubo@et.t.kanazawa-u.ac.jp, muramoto@t.kanazawa-u.ac.jp).
Cite: Karolina Nurzyńska, Mamoru Kubo, and Ken-ichiro Muramoto, "Grey Scale Texture Classification Method Comparison Considering Object and Lighting Rotation," International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 19-23, 2013.
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