Seed growing for interactive image segmentation using SVM classification with geodesic distance

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In an interactive image segmentation, the quantity of a user-given seed is known to affect the segmentation accuracy. In this Letter, we propose a seed-growing method expanding the quantity of a seed to reduce the bias of the given seed and improve the segmentation accuracy. To grow the given seed, a supervised classification framework with geodesic distance features is proposed. From a single input image, a support vector machine (SVM) classifier is trained on the seed superpixels of an input image. Other non-seed superpixels are then classified into object, background and non-seed regions by the trained classifier. In experiments, the proposed method showed promising results by improving the segmentation accuracy of existing segmentation methods in public benchmark datasets.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2017-01
Language
English
Article Type
Article
Keywords

GRAPH CUTS

Citation

ELECTRONICS LETTERS, v.53, no.1, pp.22 - 23

ISSN
0013-5194
DOI
10.1049/el.2016.3919
URI
http://hdl.handle.net/10203/222784
Appears in Collection
EE-Journal Papers(저널논문)
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