Abstract
In recent times deep neural network is widely used in different challenging problem domain like object detection, character recognition, and texture classification. There are several texture descriptors present in the pattern recognition domain to classify challenging texture datasets. Recently an efficient texture descriptor called Rotation Invariant Weber local descriptor (WLDRI), an improvement over original Weber local descriptor (WLD) has been developed to classify skin diseases. In this work, we explore the combination of WLDRI kernel in a simple deep neural network. Though the normal WLD and WLDRI efficiently classify the textures but applying WLDRI kernel into deep neural network significantly improves the performance than normal WLD and WLDRI. The present method provides an average improvement of 8.06% over WLDRI with OUTEX-10 dataset and provides 5.03% better accuracy over normal WLD with KTH-TIPS2-a dataset. In addition CMATER skin diseases dataset is used for experiment and it shows 5.74% better performance over the normal WLDRI.
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Banerjee, A., Das, N., Nasipuri, M. (2017). Texture Classification Using Deep Neural Network Based on Rotation Invariant Weber Local Descriptor. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_26
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DOI: https://doi.org/10.1007/978-981-10-4859-3_26
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