Abstract
Optimal texture feature extraction in multi-class texture classification is a challenging task. The choice of traditional texture features for texture classification and segmentation is subjective and highly application dependent with lower generalization to other textures. The Deep Neural Network (DNN) approach can overcome the problem of selecting a suitable and optimal subset of texture feature extraction method. The hidden layers inside the DNN architecture automatically extract suitable features without user intervention to give the best texture discriminating performances in multi-class texture classification environment. Our results on publicly available texture datasets show that deep learning techniques could be successfully used in image texture classification and segmentation.
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