TY - JOUR ID - 139031 TI - Noisy Textures Classification Using Deep Neural Network and Completed Local Binary Pattern JO - Journal of Machine Vision and Image Processing JA - JMVIP LA - en SN - AU - Asalimi Zamenjani, Javad AU - Shakoor, Mohammad Hossein AU - Rahmani, Mohsen AD - Ms.C Student of Computer Engineering, Arak University AD - Department of Computer Engineering, Arak University Y1 - 2022 PY - 2022 VL - 9 IS - 2 SP - 47 EP - 66 KW - Classification of texture images KW - Noisy texture images KW - Deep Neural Network KW - Local binary pattern DO - N2 - Local binary pattern is one of the most popular descriptor that widely used in feature extraction of texture images. Deep convolutional neural network is also one of the best classification methodthat provides very high accuracy. In this research, by combining the features that produced by these two methods, a structure for noisy texture classification is proposed, which provides a very high classification rate. This method is based on two extracted features. The first part uses completed local binary pattern features and in the second part the features of texture images are extracted by using the DenseNet-121 convolution deep neural network. Another motivation of this research related to feature reduction, which significantly reduces the dimensions of extracted features. It employs a shallow convolution neural network to convert the extracted features into lower number of new features. The accuracy of the proposed method has been evaluated on noisy Outex, CUReT and UIUC datasets. The classification accuracy of the proposed method for different level of noise has increased significantly compared to many advanced methods and has improved between 3 and 25%. UR - https://jmvip.sinaweb.net/article_139031.html L1 - https://jmvip.sinaweb.net/article_139031_0d9e66938c876b6c15dcf863abf60502.pdf ER -