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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. [ABSTRACT FROM AUTHOR]

The pyramidal dual-tree directional filter bank (PDTDFB) transform is a new image decomposition, which has many advantages, such as multiscale and multidirectional transform, efficient implementation, high angular resolution, low redundant ratio, and shiftable subbands. In this paper, we present a new color image segmentation algorithm based on PDTDFB domain hidden Markov tree (HMT) model. Firstly, the joint statistics and mutual information of the PDTDFB coefficients are studied. Then, the PDTDFB coefficients are modeled using a HMT model with Gaussian mixtures, which can effectively capture the intra-scale, inter-scale, and inter-direction dependencies. Finally, a color image segmentation using PDTDFB domain HMT model is developed, in which expectation–maximization (EM) parameter estimation, Bayesian multiscale raw segmentation, context based multiscale fusion, and majority-vote based color component fusion are used. Experimental evidence shows that the proposed color image segmentation algorithm has very effective segmentation results in comparison with the state-of-the-art segmentation methods recently proposed in the literature. [ABSTRACT FROM AUTHOR]