Title: Multi-Feature Fusion Method Applied in Texture Image Segmentation
Abstract: Texture patterns are complex and varied, and their applications are diverse. In many cases, the effect of image segmentation by a single texture feature extraction method is not ideal. In response to this problem, this paper proposes a multi-feature fusion method to process the texture feature extraction. The proposed method combines the gray level co-occurrence matrix (GLCM), Gabor wavelet transform and local binary pattern (LBP). It has the advantages of the above three texture feature extraction methods. Then, we use the algorithm K-means to implement the image segmentation by clustering the extracted texture features. As a result, the proposed algorithm can precisely realize the clustering for texture image segmentation. The experimental results show that the proposed algorithm is more efficient than the single texture feature extraction methods.
Publication Year: 2018
Publication Date: 2018-11-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 5
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