Salient features selection for multiclass texture classification

Authors:
Bharti Rana;R. K. Agrawal
Affiliations:
School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India;School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2012

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Abstract

Texture classification is one of the important components in texture analysis which has drawn the attention of research community during the past few decades. Various texture feature extraction techniques have been proposed in the literature. However, combining texture methods from different families has demonstrated to produce better classification at the cost of complexity of the learning model. In this paper, we have investigated three parametric test statistics (ANOVA F statistic, Welch test statistic, Adjusted Welch test statistic) to determine salient features for multiclass texture classification. The salient features are obtained from a pool of features obtained using five textural feature extraction methods. Experiments are performed on a widely used publicly available Brodatz dataset. Experimental results show that the classification error decreases significantly with the use of all the three feature selection methods with all classifiers. The reduced set of features will also lead to significant decrease in computation time of the learning model.