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http://dx.doi.org/10.3837/tiis.2022.01.012

Improved Minimum Spanning Tree based Image Segmentation with Guided Matting  

Wang, Weixing (School of Information Engineering, Chang'an University)
Tu, Angyan (Department of Computer Science and Eng., Shaoxing University)
Bergholm, Fredrik (Royal Institute of Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.1, 2022 , pp. 211-230 More about this Journal
Abstract
In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.
Keywords
Graph Theory; Guided Feathering; Image Segmentation; Minimum Spanning Tree;
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