A combined Approach Based on Fuzzy Classification and Contextual Region Growing To Image Segmentation
文献类型： 会议论文
第一作者： Mahaman Sani Chaibou
作者： Mahaman Sani Chaibou 1 ; Karim Kalti 1 ; Basel Solaiman 2 ; Mohamed Ali Mahjoub 2 ;
作者机构： 1.SAGE Research Unit ENISo, University of Sousse
2.Image & Information Processing Department (iTi), TELECOM-Bretagne/Institut TELECOM, Technopole
关键词： Image Segmentation;Fuzzy Classification;Region-growing;Context Information;Contextual Region-growing
会议名称： International Conference on Computer Graphics, Imaging and Visualization
主办单位：
页码： 172-177
摘要： We present in this paper an image segmentation approach that combines a fuzzy semantic region classification and a context based region-growing. Input image is first over-segmented. Then, prior domain knowledge is used to perform a fuzzy classification of these regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap. Each over-segmented region is represented by a vector giving its corresponding membership degrees to the different thematic labels and the whole image is therefore represented by a Regions Partition Matrix. The segmentation is achieved on this matrix instead of the image pixels through two main phases: focusing and propagation. The focusing aims at selecting seeds regions from which information propagation will be performed. The propagation phase allows to spread toward others regions and using fuzzy contextual information the needed knowledge ensuring the semantic segmentation. An application of the proposed approach on mammograms shows promising results.
分类号： TP391.41-53
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