title: Pixel classification based color image segmentation using quaternion exponent moments.
abstract: Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel-level image feature is extracted based on quaternion exponent moments (QEMs), which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel-level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state-of-the-art segmentation approaches recently proposed in the literature.
authors.affiliation: School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China. Electronic address: wxy37@126.com.
authors.forename: Xiang-Yang
authors.initials: XY
authors.lastname: Wang
authors.affiliation: School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China.
authors.forename: Zhi-Fang
authors.initials: ZF
authors.lastname: Wu
authors.affiliation: School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China.
authors.forename: Liang
authors.initials: L
authors.lastname: Chen
authors.affiliation: School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China.
authors.forename: Hong-Liang
authors.initials: HL
authors.lastname: Zheng
authors.affiliation: School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China. Electronic address: yhy_65@126.com.
authors.forename: Hong-Ying
authors.initials: HY
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003116:Color; D019277:Entropy; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: Arimoto entropy; Color image segmentation; Quaternion exponent moments; Twin support vector machines