Interactive foreground segmentation method using mean shift and graph cuts

Abstract

Purpose

The purpose of this paper is to present an efficient, interactive foreground/background image segmentation method using mean shift (MS) and graph cuts, in order to improve the segmentation performance with little user interaction.

Design/methodology/approach

By incorporating the advantages of the mean shift method and the graph cut algorithm, the proposed approach ensures the accuracy of segmentation results. First, the user marks certain pixels as foreground or background. Then the graph is constructed and the cost function composed of the boundary properties and the region properties is defined. To obtain the hidden information of user interaction, the foreground and background marks are clustered separately by the mean shift method. The region properties are determined by the minimum distances from the unmarked pixels to the foreground and background clusters. The boundary properties are determined by the relationship between the unmarked pixels and its neighbor pixels. Finally, using the graph cuts method solves the energy minimization problem to get the foreground which is of interest.

Findings

The paper presents experimental results and compares the results to other methods. It can be seen from the comparison that this method can obtain a better segmentation performance in many cases.

Originality/value

The paper incorporates the advantages of the mean shift method and the graph cut algorithm to obtain better segmentation results, even though the scene is complex.

Citation

Tian, Y., Guan, T., Wang, C., Li, L. and Liu, W. (2009), "Interactive foreground segmentation method using mean shift and graph cuts", Sensor Review, Vol. 29 No. 2, pp. 157-162. https://doi.org/10.1108/02602280910936264