In order to obtain image texture feature information and improve the accuracy of subsequent data rec-ognition in-depth,this paper proposed a hierarchical fuzzy mining algorithm for local features of single images.First-ly,the histogram equalization method was used to concentrate the gray values in images into gray-level regions corre-spondingly and transform them from a non-uniform distribution to a uniform distribution,thereby expanding the dy-namic range of the gray level of the pixel.Then,the complexity and difference degree of local image features were an-alyzed to calculate the gray level of adjacent templates,thus obtaining a matrix of local complexity and difference.Furthermore,the Laplace algorithm was adopted for recommending and classifying the local features.According to the recommended level,selected features were hierarchically mined.On this basis,the hierarchical fuzzy mining of local features of a single image was achieved.Experimental results show that the proposed algorithm can accurately mine image features as well as complete texture information at different levels and maintain mining time within 0.25s.
关键词
单幅图像/局部特征/特征分层模糊挖掘/差异度矩阵/局部差异度
Key words
Single image/Local features/Feature hierarchical fuzzy mining/Difference matrix/Local difference
维普
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