| Citation: | WANG Yuling, LI Ming, LI Junhua, et al. Texture image classification based on BoF model with multi-feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1869-1877. doi: 10.13700/j.bh.1001-5965.2017.0720(in Chinese)
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The obvious shortcomings of bag of feature (BoF) model are lack of spatial and geometric information in image representation, and poor description of the content of texture image. To solve these problems, we proposed a texture image classification method based on BoF model with multi-feature fusion. The method fuses gray gradient co-occurrence matrix (GGCM) and scale-invariant feature transform (SIFT) as the basic feature description of texture image, uses a dynamic weight to identify energy analysis for the optimal parameter feature selection, quantifies texture feature by BoF, then applies support vector machine to train and predict the image, and finally obtains the classification results. The experimental results show that the proposed method has better performance of texture classification into rotated texture, twisted texture, edge fuzzy texture, light changing texture, messy texture, etc. The average classification accuracy of the proposed method on the UIUC texture database increases by 12.8% and 7.9% respectively compared with the conventional BoF model and concave-convex partition (CCP) methods, which indicates that the proposed method has higher accuracy and better robustness for texture image classification.
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