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

ShuPing Xiao

doi:10.1117/12.2604674

5 October 2021 Texture image recognition based on feature layer fusion and double probabilistic neural network

ShuPing Xiao

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191102 (2021) https://doi.org/10.1117/12.2604674
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

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.

Citation Download Citation

ShuPing Xiao "Texture image recognition based on feature layer fusion and double probabilistic neural network", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191102 (5 October 2021); https://doi.org/10.1117/12.2604674

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