[IJ] Graph Cuts-based Automatic Color Image Segmentation using Mean Shift Analysis

Under Review
(IEEE Transactions on Systems, Man and Cybernetics, Part B - SCI)

Abstract.

A graph cuts method has recently attracted a lot of attention for image segmentation, as it can provide exact global optimasolutions for certain energy functions composed of data (regional) terms estimated in feature space and smoothness (boundary) termsestimated in an image domain. Although previous approaches using graph cuts have shown good performance for image segmentation,they manually obtained prior information to estimate the data term. To automatically estimate the data term, GMM (Gaussian mixturemodels) are generally used, but it is practicable only for classes with a hyper-spherical or hyper-ellipsoidal shape in feature space, as theclass is represented based on the covariance matrix centered on the mean. For arbitrary-shaped classes, this paper proposes a graphcuts-based automatic image segmentation method using mean shift analysis. We use the set of mean trajectories towards each modefrom initial means as prior information to estimate the data term, and the data that are not included in the set of prior information arecovered by a smoothness term that can preserve discontinuities in an image domain. Then, a graph cuts method is used to globallyoptimize the energy function. The main drawback of the mean shift procedures is it greatly consumes computational time. To tackle thisdrawback, we transform features in continuous feature space, i.e. L*u*v* color space in this paper, into a discrete 3D grid, and use 3Dkernel based on the first moment to move the means to modes in the grid. In the experiments, we investigated the problems of meanshift-based and normalized cuts-based image segmentation methods that have recently become popular methods and graph cuts-basedautomatic image segmentation using GMM. The proposed method showed better performance than the previous three methods onBerkeley Segmentation Dataset.
Keywords: Color Image Segmentation, Graph Cuts, Mean Shift Analysis.

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2008.