Classification of textured images based on new information fusion methods<\/span><\/p>\n Le nom de la revue<\/span><\/strong><\/span><\/p>\n Ann\u00e9e :<\/span><\/strong>2019<\/span><\/p>\n [\/su_box]\u00a0<\/span><\/p>\n [su_box title=\u00a0\u00bb\u00a0\u00bb style=\u00a0\u00bbglass\u00a0\u00bb box_color=\u00a0\u00bb#b91268″ title_color=\u00a0\u00bb#ffffff\u00a0\u00bb radius=\u00a0\u00bb15″]<\/p>\n R\u00e9sum\u00e9<\/span><\/strong><\/span><\/p>\n This work presents supervised classification algorithms based on information fusion for textured-images segmentation. Gabor features are efficient in finding class boundaries, whereas grey-Level co-occurrence matrix features are favorable in the areas within the classes. Moreover, the wavelets can represent textures at different scales and offer great discriminatory power between textures with strong resemblances. This motivates us to combine these three kinds of features with improving image segmentation. In the first step, the proposed method applied those three feature extraction strategies on textured images to get more information. After that in the second step, the estimated feature vector of each pixel is sent to the neural networks classifier for pre-labelling. Then, in the third step of the proposed method, a classifier fusion method used to combine the scores obtained by the neural networks for each pixel. Finally, in the last step, to obtain more precise segmentation results, the scores within a sliding window are combined. The performance of the proposed segmentation algorithms was evaluated on synthetic images from Brodatz and DTD datasets. The obtained classification results from the proposed fusions system lead to higher classification precision compared to applying a single classifier on the textured images.<\/span><\/p>\n [\/su_box]\u00a0<\/span><\/p>\n
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