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Texture image recognition based on feature layer fusion and double probabilistic neural network

Texture recognition is study on the texture in a large area, the goal is to classify the texture in the area and determine which category they belong to. Its basic content is to use the texture feature extraction method to get the texture feature, and then use the appropriate classifier to classify. In this paper, Principal Component Analysis (PCA) is used to reduce the feature dimension of the original image to get the texture image features with lower dimension first. Then the features are fused by the coarse-to-fine strategy, and finally the texture is classified by the double probability neural network. Experimental results show that, compared with the common classification methods, the proposed texture image recognition method improves the correct recognition rate.