An improvement of color image segmentation through projective clustering
An improvement of color image segmentation through projective clustering
复制标题
通过投影聚类改进彩色图像分割
DOI：
10.1109/iri.2012.6303004
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Wei
中科院分区：
文献类型：
--
作者：
Song Gao;Chengcui Zhang;Wei
图像分割作为将一幅数字图像分割成多个片段的过程，在图像检索、医学检查、计算机取证等领域有着广泛的应用。将聚类方法作为一种解决方案应用于图像的单个或多个特征空间，例如颜色、强度或纹理，以便将共享某些视觉特征的相似像素分组。在给定特定颜色图像的情况下，并非颜色空间中的所有特征(例如RGB、HSV或Lab)在描述片段的视觉特征方面都同样有效。本文提出了一种基于爬山的投影聚类算法(HCPC)，该算法以EPCH(一种基于直方图构造的高效投影聚类技术)为主要框架，采用爬山算法进行密集区域检测，用于彩色图像分割，从而在给定特征空间的子空间内找到感兴趣的簇(段)。在HSV(色调、饱和度和值)颜色空间的基础上，提出了一种新的特征空间HSVrVgVb。实验结果表明，与爬山算法(用于有效的基于颜色的图像分割)相比，当特征空间的维度较高时，我们提出的算法具有更强的伸缩性，并且可以得到与之相当的分割结果。
Image segmentation as the processing of partitioning a digital image into multiple segments has wide applications, such as image retrieval, medical inspection, and computer forensics. Clustering methods as one solution are applied on a single or multiple feature spaces of an image, such as color, intensity, or texture, in order to group similar pixels that share certain visual characteristics. Given a particular color image, not all features from a color space, such as RGB, HSV, or Lab, are equally effective in describing the visual characteristics of segments. In this paper, we propose a projective clustering algorithm HCPC (Hill-Climbing based Projective Clustering) which utilizes EPCH (an efficient projective clustering technique by histogram construction) as the main framework and hill-climbing algorithm for dense region detection, for color image segmentation, thereby finding interesting clusters (segments) within subspaces of a given feature space. A new feature space, named HSVrVgVb, is also explored which is derived from HSV (Hue, Saturation, and Value) color space. The experimental results show that compared with hill-climbing algorithm (for efficient color-based image segmentation), our proposed algorithm is more scalable when the dimensionality of feature space is high, and also generates comparable segmentation results.