A novel neural network based image descriptor for texture classification

Nowadays, image processing and artificial intelligence have become popular science areas. The one of the major problems of the image processing is texture classification. Therefore, many methods have been presented about texture classification. In this article, a new textural feature extraction method is proposed. In this method, the feed forward neural networks are utilized as a feature extractor. The main purpose of the proposed method is to show feature extraction capability of the feed forward neural network. This descriptor consists of 3 x 3 overlapping blocks division, creating feature extraction network by using row and column pixels of the block, calculating feature value, normalization and histogram extraction. Firstly, image is divided into 3 x 3 size of overlapping blocks and pixels of each block are selected to create feed forward networks. To calculate the weights, neighbor pixel values and the signum function are used together. Tangent hyperbolic function is utilized as activation function in these networks. PCA (Principle Component Analysis) reduces feature dimensionality and LDA (Linear Discriminant Analysis) is chosen as classifier. In order to obtain the experimental results, the commonly used texture datasets were used with variable parameters. These datasets are UIUC, Outex and USPTex. The classification accuracies were calculated as 90.82%, 89.62% and 93.83% for these datasets respectively. The results were compared with the related 16 methods and the proposed method achieved the best performance among them. The space complexity of this method also calculated and the cost of the proposed method was given in the experiments. The computational costs result of this method demonstrates that the proposed neural network based image descriptor method has low complexity. The results clearly illustrated that the proposed textural image descriptor extracts distinctive features with short execution time, has simple mathematical background, is good discriminator and outperforms. (C) 2019 Elsevier B.V. All rights reserved.