An improvement of color image segmentation through projective clustering
Song Gao, Chengcui Zhang, Wei-Bang Chen · 2012
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.