Segmentation of color images using multiscale clustering and graph theoretic region synthesis
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
复制标题
DOI：
10.1109/tsmca.2004.832820
复制
发表时间：
2005-03
期刊：
影响因子：
--
通讯作者：
S. Makrogiannis;G. Economou;S. Fotopoulos;N. Bourbakis
中科院分区：
文献类型：
--
作者：
S. Makrogiannis;G. Economou;S. Fotopoulos;N. Bourbakis
提出了一种多分辨率彩色图像分割方法，该方法结合了基于区域的分割和聚类分析方法的主要原理。这项工作的贡献可以分为两个部分。在第一部分中，提出了一种多尺度相异度度量，该度量利用特征变换操作来度量区域间相对于图像主要集群的接近程度的关系。作为这个过程的一部分，一个原始的方法也提出了使用非参数聚类生成的图像信息的多尺度表示。在第二部分中，提出了一种图论算法来合成区域并产生最终的分割结果。后一种算法出现在聚类算法的背景下的模糊相似关系的简要分析。分析表明，利用相似关系理论可以充分而简洁地描述一般的分割方法。该方案产生了令人满意的结果，并通过比较它的效率表明：1）单尺度版本的相异性度量和2）在文献中提出的几个早期的图论合并方法。最后，多尺度处理和区域合成属性验证我们的方法的应用，如目标识别，图像检索，和人类视觉感知的仿真。
A multiresolution color image segmentation approach is presented that incorporates the main principles of region-based segmentation and cluster-analysis approaches. The contribution of This work may be divided into two parts. In the first part, a multiscale dissimilarity measure is proposed that makes use of a feature transformation operation to measure the interregion relations with respect to their proximity to the main clusters of the image. As a part of this process, an original approach is also presented to generate a multiscale representation of the image information using nonparametric clustering. In the second part, a graph theoretic algorithm is proposed to synthesize regions and produce the final segmentation results. The latter algorithm emerged from a brief analysis of fuzzy similarity relations in the context of clustering algorithms. This analysis indicates that the segmentation methods in general may be formulated sufficiently and concisely by means of similarity relations theory. The proposed scheme produces satisfying results and its efficiency is indicated by comparing it with: 1) the single scale version of dissimilarity measure and 2) several earlier graph theoretic merging approaches proposed in the literature. Finally, the multiscale processing and region-synthesis properties validate our method for applications, such as object recognition, image retrieval, and emulation of human visual perception.