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.
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Rana, B., Agrawal, R.K. (2013). Salient Features Selection for Multiclass Texture Classification. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_18
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DOI: https://doi.org/10.1007/978-3-642-37807-2_18
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