Abstract
This paper proposes an interactive image-segmentation method which is based on superpixel. To achieve fast segmentation, the method is used to establish a Graphcut model using superpixels as nodes, and a new energy function is proposed. Experimental results demonstrate that the authors' method has excellent per- formance in terms of segmentation accuracy and computation efficiency compared with other segmentation algo- rithm based on pixels.
Highlights
The idea of image segmentation is to distinguish pixels belonging to foreground or background
[1] Graphcut model and use superpixels generated by Simple Linear Iterative Clustering (SLIC) as nodes shown in Figure 1(a) We pre-segment images into superpixels such as i and j, and extend the seed pixels given from user into seed superpixels
A new energy function is proposed which is more suitable to superpixel-based model
Summary
The idea of image segmentation is to distinguish pixels belonging to foreground or background. An interactive segmentation-method based on graph model was proposed by Boykov, Y.Y.[1] in 2001. The innovation can be divided into two categories: energy function based methods [2] [3] and superpixel-based methods [4]. Energy function-based methods create new background/foreground models to compute energy function, such as Grabcut [2], it improves accuracy at the expense of efficiency. The superpixel-based methods, such as lazy snapping [4], achieve more excellent results than energy function-based methods. We propose a superpixel-based method to improve both accuracy and efficiency of Graphcut[1]. We pre-segment the original image into small areas, and apply them to set up graph models for Graphcut. 2) A new energy function is created, and it is more suitable to superpixel-based model. 3) We compare our method with two state-of-the-art interaction segmentation mehods on the CMU-Cornell iCoseg Dataset [5], as our method can be a preprocessing of co-segmentation task
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