文章基本信息

标题：An Adaptive Color Texture Segmentation Using Similarity Measure of Symbolic Object Approach
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作者：Dr. G. Uma Maheswari1 ; Dr. K. Ramar2 ; Dr. D. Manimegalai1 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2011
卷号：4
期号：4
出版社：SERSC
摘要：Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can be used for the segmentation of different textured images. This paper presents a new approach for color texture segmentation using Haralick’s features extracted from color co-occurrence matrices. The originality of this approach is to select the most discriminating color texture features extracted from the color co-occurrence. Symbolic Object Approach is used for achieving texture segmentation.
关键词：Color co-occurrence matrix; Haralick’s features; texture segmentation;symbolic object approach