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

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.

References

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      • 5. Ming-Yu, L., Tuzel, O., Ramalingam, S., Chellappa, R.: ‘Entropy rate superpixel segmentation’. IEEE Conf. Computer Vision and Pattern Recognition, 2011, pp. 20972104.
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      • 6. Gulshan, V., Rother, C., Criminisi, A., Blake, A., Zisserman, A.: ‘Geodesic star convexity for interactive image segmentation’. IEEE Conf. Computer Vision and Pattern Recognition, April 2010, pp. 31293136.
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