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2018 | OriginalPaper | Chapter

A Kernel Method with Manifold Regularization for Interactive Segmentation

Authors : Haohao Chen, En Zhu, Xinwang Liu, Junnan Zhang, Jianping Yin

Published in: Theoretical Computer Science

Publisher: Springer Singapore

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Abstract

Interactive segmentation has been successfully applied to various applications such as image editing, computer vision, image identification. Most of existing methods require interaction for each single image segmentation, which costs too much labor interactions. To address this issue, we propose a kernel based semi-supervised learning framework with manifold regularization for interactive image segmentation in this paper. Specifically, by manifold regularization, our algorithm makes similar superpixel pair bearing the same label. Moreover, the learned classifier on one single image is directly used to similar images for segmentation. Extensive experimental results demonstrate the effectiveness of the proposed approach.

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Literature
1.
Boykov, Y.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. ICCV 1, 105–112 (2001)
2.
Wu, J., Zhao, Y., Zhu, J.Y., et al.: MILCut: a sweeping line multiple instance learning paradigm for interactive image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 256–263. IEEE Computer Society (2014)
3.
Grady, L.: Random walks for image segmentation. IEEE Computer Society (2006)
4.
Gulshan, V., Rother, C., Criminisi, A., et al.: Geodesic star convexity for interactive image segmentation. In: Computer Vision and Pattern Recognition, pp. 3129–3136. IEEE (2010)
5.
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)CrossRef
6.
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut": interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)CrossRef
7.
Dai, J., He, K., Sun, J.: BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation 1635–1643 (2015)
8.
Rupprecht, C., Peter, L., Navab, N.: Image segmentation in twenty questions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3314–3322. IEEE Computer Society (2015)
9.
Liang, L., Grauman, K.: Beyond comparing image pairs: setwise active learning for relative attributes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 208–215. IEEE Computer Society (2014)
10.
Wang, T., Sun, Q., Ge, Q., et al.: Interactive image segmentation via pairwise likelihood learning. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2957–2963 (2017)
11.
Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recogn. 46(3), 1020–1038 (2013)CrossRef
12.
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. JMLR.org (2006)
13.
Achanta, R., Shaji, A., Smith, K.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274 (2012)CrossRef
14.
Andrade, F., Carrera, E.V.: Supervised evaluation of seed-based interactive image segmentation algorithms. In: Signal Processing, Images and Computer Vision, pp. 1–7. IEEE (2015)
15.
Casaca, W., Nonato, L.G., Taubin, G.: Laplacian coordinates for seeded image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 384–391. IEEE Computer Society (2014)
16.
Dong, X., Shen, J., Shao, L., Van Gool, L.: Sub-Markov random walk for image segmentation. IEEE Trans. Image Process. 25(2), 516–527 (2016)MathSciNetCrossRef
17.
Kim, T.H., Lee, K.M., Lee, S.U.: Nonparametric higher-order learning for interactive segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3201–3208 (2010)
18.
Fan, Q., Zhong, F., Lischinski, D.: JumpCut: non-successive mask transfer and interpolation for video cutout. ACM Trans. Graph. 34(6), 195 (2015)CrossRef
Metadata
Title
A Kernel Method with Manifold Regularization for Interactive Segmentation
Authors
Haohao Chen
En Zhu
Xinwang Liu
Junnan Zhang
Jianping Yin
Copyright Year
2018
Publisher
Springer Singapore
Book
Theoretical Computer Science

Print ISBN: 978-981-13-2711-7

Electronic ISBN: 978-981-13-2712-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-2712-4_10

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