Kernel-based spectral color image segmentation

doi:10.1364/JOSAA.25.002805

Kernel-based spectral color image segmentation

Hongyu Li, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, and I-fan Shen »View Author Affiliations

JOSA A, Vol. 25, Issue 11, pp. 2805-2816 (2008)
http://dx.doi.org/10.1364/JOSAA.25.002805

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Abstract

In this work, we propose a new algorithm for spectral color image segmentation based on the use of a kernel matrix. A cost function for spectral kernel clustering is introduced to measure the correlation between clusters. An efficient multiscale method is presented for accelerating spectral color image segmentation. The multiscale strategy uses the lattice geometry of images to construct an image pyramid whose hierarchy provides a framework for rapidly estimating eigenvectors of normalized kernel matrices. To prevent the boundaries from deteriorating, the image size on the top level of the pyramid is generally required to be around $75 \times 75$ , where the eigenvectors of normalized kernel matrices would be approximately solved by the Nyström method. Within this hierarchical structure, the coarse solution is increasingly propagated to finer levels and is refined using subspace iteration. In addition, to make full use of the abundant color information contained in spectral color images, we propose using spectrum extension to incorporate the geometric features of spectra into similarity measures. Experimental results have shown that the proposed method can perform significantly well in spectral color image segmentation as well as speed up the approximation of the eigenvectors of normalized kernel matrices.

OCIS Codes
(330.1720) Vision, color, and visual optics : Color vision
(330.6180) Vision, color, and visual optics : Spectral discrimination
(100.4145) Image processing : Motion, hyperspectral image processing

ToC Category:
Image Processing

History
Original Manuscript: June 23, 2008
Manuscript Accepted: August 12, 2008
Published: October 23, 2008

Virtual Issues
Vol. 4, Iss. 1 Virtual Journal for Biomedical Optics

Citation
Hongyu Li, Vladimir Bochko, Timo Jaaskelainen, Jussi Parkkinen, and I-fan Shen, "Kernel-based spectral color image segmentation," J. Opt. Soc. Am. A 25, 2805-2816 (2008)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-25-11-2805

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References

P. Paclík, R. P. W. Duin, G. M. P. van Kempen, and R. Kohlus, “Segmentation of multispectral images using the combined classifier approach,” Image Vis. Comput. 21, 473-482 (2003). [CrossRef]
J. L. Crespo, R. J. Duro, and F. L. Pena, “Gaussian synapse ANNs in multi- and hyperspectral image data analysis,” IEEE Trans. Instrum. Meas. 52, 724-732 (2003). [CrossRef]
S. K. Pal and P. Mitra, “Multispectral image segmentation using the rough-set-initialized EM algorithm,” IEEE Trans. Geosci. Remote Sens. 40, 2495-2501 (2002). [CrossRef]
G. Mercier, S. Derrode, and M. Lennon, “Hyperspectral image segmentation with Markov chain model,” in IEEE International Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS'03) (IEEE, 2003), Vol. 122, pp. 21-25.
A. Mohammad-Djafari, N. Bali, and A. Mohammadpour, “Hierarchical Markovian models for hyperspectral image segmentation,” in International Workshop on Intelligent Computing in Pattern Analysis/Systems, IWICPAS (Springer, 2006), pp. 416-424.
H. Kwon and N. M. Nasrabadi, “Kernel matched subspace detectors for hyperspectral target detection,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 178-194 (2006). [CrossRef] [PubMed]
Y. Weiss, “Segmentation using eigenvectors: a unifying view,” in IEEE International Conference on Computer Vision (ICCV'99) (IEEE, 1999), pp. 975-982. [CrossRef]
A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: analysis and an algorithm,” in Proceedings of the Neural Information Processing Systems, NIPS'01 (MIT, 2001), Vol. 14, pp. 849-856.
S. X. Yu and J. Shi, “Multiclass spectral clustering,” in IEEE International Conference on Computor Vision (ICCV'03) (IEEE, 2003), pp. 313-319. [CrossRef]
N. Cristianini, J. Shawe-Taylor, and J. S. Kandola, “Spectral kernel methods for clustering,” in Proceedings of the Neural Information Processing Systems, NIPS'01 (MIT, 2001), pp. 649-655.
C. Fowlkes, S. Belongie, F. R. K. Chung, and J. Malik, “Spectral grouping using the Nyström method,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 214-225 (2004). [CrossRef] [PubMed]
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis (Cambridge U. Press, 2004). [CrossRef]
University of Joensuu Color Group, “Spectral database,” http://spectral.joensuu.fi/.
F. R. Bach and M. I. Jordan, “Learning spectral clustering,” in Proceedings of the Neural Information Processing Systems, NIPS'03 (MIT, 2003), http://books.nips.cc/papers/files/nips16/NIPS2003_AA39.pdf.
D. Achlioptas, F. McSherry, and B. Schölkopf, “Sampling techniques for kernel methods,” in Proceedings of the Neural Information Processing Systems, NIPS'01, (MIT, 2001), pp. 335-342.
C. K. I. Williams and M. Seeger, “Using the Nyström method to speed up kernel machines,” in Proceedings of the Neural Information Processing Systems, NIPS'00 (MIT, 2000), pp. 682-688.
T. Cour, F. Bénézit, and J. Shi, “Spectral segmentation with multiscale graph decomposition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CPVR 2005 (IEEE, 2005), pp. 1124-1131.
E. Sharon, M. Galun, D. Sharon, R. Basri, and A. Brandt, “Hierarchy and adaptivity in segmenting visual scenes,” Nature 442, 810-813 (2006). [CrossRef] [PubMed]
D. Tolliver, R. T. Collins, and S. Baker, “Multilevel spectral partitioning for efficient image segmentation and tracking,” in the Seventh IEEE Workshop on Application of Computer Vision (WACA/MOTION'OE) (IEEE, 2005), Vol. 1, pp. 414-420. [CrossRef]
B. N. Parlett, The Symmetric Eigenvalue Problem (Prentice Hall, 1998). [CrossRef]
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Toward objective evaluation of image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 929-944 (2007). [CrossRef] [PubMed]
R. O. Duda, P. E. Hart, and D. F. Stork, Pattern Classification (Wiley, 2001).
T. Cour, F. Benezit, and J. Shi, “Multiscale NCut Image Segmentation Toolbar,” http://www.seas.upenn.edu/~timothee/software/ncut_multiscale/ncut_multiscale.html.

Achlioptas, D.
Bach, F. R.
Baker, S.
Bali, N.
Basri, R.
Belongie, S.
Benezit, F.
Bénézit, F.
Brandt, A.
Chung, F. R. K.
Collins, R. T.
Cour, T.
Crespo, J. L.
Cristianini, N.
Derrode, S.
Duda, R. O.
Duin, R. P. W.
Duro, R. J.
Fowlkes, C.
Galun, M.
Hart, P. E.
Hebert, M.
Jordan, M. I.
Kandola, J. S.
Kohlus, R.
Kwon, H.
Lennon, M.
Malik, J.
McSherry, F.
Mercier, G.
Mitra, P.
Mohammad-Djafari, A.
Mohammadpour, A.
Nasrabadi, N. M.
Ng, A. Y.
Paclík, P.
Pal, S. K.
Pantofaru, C.
Parlett, B. N.
Pena, F. L.
Schölkopf, B.
Seeger, M.
Sharon, D.
Sharon, E.
Shawe-Taylor, J.
Shi, J.
Stork, D. F.
Tolliver, D.
Unnikrishnan, R.
van Kempen, G. M. P.
Weiss, Y.
Williams, C. K. I.
Yu, S. X.

IEEE Trans. Geosci. Remote Sens.

S. K. Pal and P. Mitra, “Multispectral image segmentation using the rough-set-initialized EM algorithm,” IEEE Trans. Geosci. Remote Sens. 40, 2495-2501 (2002). [CrossRef]

IEEE Trans. Instrum. Meas.

J. L. Crespo, R. J. Duro, and F. L. Pena, “Gaussian synapse ANNs in multi- and hyperspectral image data analysis,” IEEE Trans. Instrum. Meas. 52, 724-732 (2003). [CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

H. Kwon and N. M. Nasrabadi, “Kernel matched subspace detectors for hyperspectral target detection,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 178-194 (2006). [CrossRef] [PubMed]
C. Fowlkes, S. Belongie, F. R. K. Chung, and J. Malik, “Spectral grouping using the Nyström method,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 214-225 (2004). [CrossRef] [PubMed]
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Toward objective evaluation of image segmentation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 929-944 (2007). [CrossRef] [PubMed]

Image Vis. Comput.

P. Paclík, R. P. W. Duin, G. M. P. van Kempen, and R. Kohlus, “Segmentation of multispectral images using the combined classifier approach,” Image Vis. Comput. 21, 473-482 (2003). [CrossRef]

Nature

E. Sharon, M. Galun, D. Sharon, R. Basri, and A. Brandt, “Hierarchy and adaptivity in segmenting visual scenes,” Nature 442, 810-813 (2006). [CrossRef] [PubMed]

Other

D. Tolliver, R. T. Collins, and S. Baker, “Multilevel spectral partitioning for efficient image segmentation and tracking,” in the Seventh IEEE Workshop on Application of Computer Vision (WACA/MOTION'OE) (IEEE, 2005), Vol. 1, pp. 414-420. [CrossRef]
B. N. Parlett, The Symmetric Eigenvalue Problem (Prentice Hall, 1998). [CrossRef]
R. O. Duda, P. E. Hart, and D. F. Stork, Pattern Classification (Wiley, 2001).
T. Cour, F. Benezit, and J. Shi, “Multiscale NCut Image Segmentation Toolbar,” http://www.seas.upenn.edu/~timothee/software/ncut_multiscale/ncut_multiscale.html.
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis (Cambridge U. Press, 2004). [CrossRef]
University of Joensuu Color Group, “Spectral database,” http://spectral.joensuu.fi/.
F. R. Bach and M. I. Jordan, “Learning spectral clustering,” in Proceedings of the Neural Information Processing Systems, NIPS'03 (MIT, 2003), http://books.nips.cc/papers/files/nips16/NIPS2003_AA39.pdf.
D. Achlioptas, F. McSherry, and B. Schölkopf, “Sampling techniques for kernel methods,” in Proceedings of the Neural Information Processing Systems, NIPS'01, (MIT, 2001), pp. 335-342.
C. K. I. Williams and M. Seeger, “Using the Nyström method to speed up kernel machines,” in Proceedings of the Neural Information Processing Systems, NIPS'00 (MIT, 2000), pp. 682-688.
T. Cour, F. Bénézit, and J. Shi, “Spectral segmentation with multiscale graph decomposition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CPVR 2005 (IEEE, 2005), pp. 1124-1131.
G. Mercier, S. Derrode, and M. Lennon, “Hyperspectral image segmentation with Markov chain model,” in IEEE International Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS'03) (IEEE, 2003), Vol. 122, pp. 21-25.
A. Mohammad-Djafari, N. Bali, and A. Mohammadpour, “Hierarchical Markovian models for hyperspectral image segmentation,” in International Workshop on Intelligent Computing in Pattern Analysis/Systems, IWICPAS (Springer, 2006), pp. 416-424.
Y. Weiss, “Segmentation using eigenvectors: a unifying view,” in IEEE International Conference on Computer Vision (ICCV'99) (IEEE, 1999), pp. 975-982. [CrossRef]
A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: analysis and an algorithm,” in Proceedings of the Neural Information Processing Systems, NIPS'01 (MIT, 2001), Vol. 14, pp. 849-856.
S. X. Yu and J. Shi, “Multiclass spectral clustering,” in IEEE International Conference on Computor Vision (ICCV'03) (IEEE, 2003), pp. 313-319. [CrossRef]
N. Cristianini, J. Shawe-Taylor, and J. S. Kandola, “Spectral kernel methods for clustering,” in Proceedings of the Neural Information Processing Systems, NIPS'01 (MIT, 2001), pp. 649-655.

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