Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Image Segmentation with Superpixel Based Covariance Descriptor

Authors : Xianbin Gu, Martin Purvis

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

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Phishing Detection on Twitter Streams
next chapter Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals
Appendix
Available only for authorised users
Literature
1.
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. TPAMI 33(5), 898–916 (2011)CrossRef
2.
Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Fast and Simple Computations on Tensors with Log-euclidean Metrics, Phd thesis, INRIA (2005)
3.
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
4.
5.
Chen, J., Yang, J.: Robust subspace segmentation via low-rank representation. IEEE Trans. Cybern. 44(8), 1432–1445 (2014)CrossRef
6.
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
7.
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRef
8.
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
9.
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.
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)
Metadata
Title
Image Segmentation with Superpixel Based Covariance Descriptor
Authors
Xianbin Gu
Martin Purvis
Copyright Year
2016
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-42995-3

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

Copyright Year: 2016

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

Premium Partner

    Image Credits