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2016 | OriginalPaper | Buchkapitel

Image Segmentation with Superpixel Based Covariance Descriptor

verfasst von : Xianbin Gu, Martin Purvis

Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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Abstract

This paper investigates the problem of image segmentation using superpixels. We propose two approaches to enhance the discriminative ability of the superpixel’s covariance descriptors. In the first one, we employ the Log-Euclidean distance as the metric on the covariance manifolds, and then use the RBF kernel to measure the similarities between covariance descriptors. The second method is focused on extracting the subspace structure of the set of covariance descriptors by extending a low rank representation algorithm on to the covariance manifolds. Experiments are carried out with the Berkly Segmentation Dataset, and compared with the state-of-the-art segmentation algorithms, both methods are competitive.

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Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. TPAMI 33(5), 898–916 (2011)CrossRef

Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Fast and Simple Computations on Tensors with Log-euclidean Metrics, Phd thesis, INRIA (2005)

Cai, J.-F., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010)MathSciNetCrossRefMATH

Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)MathSciNetCrossRefMATH

Chen, J., Yang, J.: Robust subspace segmentation via low-rank representation. IEEE Trans. Cybern. 44(8), 1432–1445 (2014)CrossRef

Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRef

Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRef

Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet another survey on image segmentation: region and boundary information integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)CrossRef

Fu, Y., Gao, J., Hong, X., Tien, D.: Low rank representation on Riemannian manifold of symmetric positive definite matrices. In: Proceedings of SDM. SIAM (2015)

10.

Ganesh, A., Lin, Z., Wright, J., Wu, L., Chen, M., Ma, Y.: Fast algorithms for recovering a corrupted low-rank matrix. In: 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 213–216. IEEE (2009)

11.

Gu, X., Deng, J.D., Purvis, M.K.: Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4403–4406. IEEE (2014)

12.

Habiboğlu, Y.H., Günay, O., Çetin, A.E.: Covariance matrix-based fire and flame detection method in video. Mach. Vis. Appl. 23(6), 1103–1113 (2012)CrossRef

13.

Harandi, M.T., Sanderson, C., Wiliem, A., Lovell, B.C.: Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures. In: 2012 IEEE Workshop on Applications of Computer Vision (WACV), pp. 433–439. IEEE (2012)

14.

Jayasumana, S., Hartley, R., Salzmann, M., Li, H., Harandi, M.: Kernel methods on Riemannian manifolds with Gaussian RBF kernels. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2464–2477 (2015). IEEECrossRef

15.

Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetCrossRefMATH

16.

Li, Z., Wu, X.-M., Chang, S.-F.: Segmentation using superpixels: a bipartite graph partitioning approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 789–796. IEEE (2012)

17.

Lin, Z., Chen, M., Ma, Y.: The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices (2010). arXiv preprint arXiv:1009.5055

18.

Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)CrossRef

19.

Liu, G., Yan, S.: Latent low-rank representation for subspace segmentation and feature extraction. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1615–1622. IEEE (2011)

20.

Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV 2001, vol. 2, pp. 416–423 (2001)

21.

Meilă, M.: Comparing clusterings: an axiomatic view. In: ICML 2005, pp. 577–584. ACM (2005)

22.

Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. TPAMI 29(6), 929–944 (2007)CrossRef

23.

Wang, B., Hu, Y., Gao, J., Sun, Y., Yin, B.: Kernelized low rank representation on grassmann manifolds (2015). arXiv preprint arXiv:1504.01806

24.

Wang, B., Hu, Y., Gao, J., Sun, Y., Yin, B.: Low rank representation on grassmann manifolds: an extrinsic perspective (2015). arXiv preprint arXiv:1504.01807

25.

Wang, X., Li, H., Masnou, S., Chen, L.: Sparse coding and mid-level superpixel-feature for \(\ell \) \(_\text{0 }\)-graph based unsupervised image segmentation. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 160–168. Springer, Heidelberg (2013)CrossRef

26.

Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. In: Advances in Neural Information Processing Systems, pp. 2080–2088 (2009)

27.

Xie, Y., Ho, J., Vemuri, B.: On a nonlinear generalization of sparse coding and dictionary learning. In: Proceedings of the International Conference on Machine Learning, p. 1480. NIH Public Access (2013)

Titel: Image Segmentation with Superpixel Based Covariance Descriptor
verfasst von: Xianbin Gu
Martin Purvis
Verlag: Springer International Publishing
Buch: Trends and Applications in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-42995-3

Electronic ISBN: 978-3-319-42996-0

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-42996-0_13

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