Abstract
This paper proposes a methodology to obtain a fully automatic color segmentation algorithm based on the Normalized Cut (Ncut) proposed by Shi and Malik, using recent findings in color perception. A weighting matrix computed using a perceptually uniform color space (CIE \(L^*a^*b^*\)) and color distance formulae correlated with the visually perceived color differences (CIE94 and CIEDE2000); a stopping condition related to perceptual criteria; an automatic parameters setting required to compute the affinity matrix are proposed. To test the proposed methodology, a wide study about the influence of the color space choice, different stopping conditions, and different similarity measurements is carried out. These alternatives are exhaustively evaluated using perception-related measurements (S-CIELAB) and general segmentation evaluation metrics applied to the 500 images of the Berkeley database. The results showed that the proposed method outperforms Ncut based on other color spaces, similarity measure or stopping conditions. Furthermore, the usability of the method is increased by replacing the manual parameter setting for an automatic.
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References
Plataniotis, K.N., Venetsanopoulos, A.N.: Color Imagen Processing and Applications. Springer, New York, Inc. (2000)
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognit. 34(12), 22592281 (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22(8), 888–905 (2000)
Chen, J.-L., Bai, Z., Hamann, B., Ligocki, T.J.: A normalized-cut algorithm for hierarchical vector field data segmentation. In: Proceedings of SPIE–The International Society for Optical Engineering, vol. 5009, pp. 79–90 (2003)
Naotoshi, S.: Normalized cuts and image segmentation. http://note.sonots.com/SciSoftware/NcutImageSegmentation.html (2006)
Sun, F., He, J.-P.A.: Normalized cuts based image segmentation method. In: 2009 2nd International Conference on Information and Computing Science, ICIC 2(5169079), pp. 333–336 (2009)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43(1), 7–27 (2001)
Riaz, F., Silva, F.B., Ribeiro, M.D., Coimbra, M.T.: Impact of visual features on the segmentation of gastroenterology images using normalized Cuts. IEEE T. Bio-Med Eng. 60(5), 1191–1201 (2013)
Wang, X., Zhu, C., Bichot, C.-E., Masnou, S.: Graph-based image segmentation using weighted color patch. In: 2013 IEEE International Conference on Image Processing, ICIP 2013. 6738837, pp. 4064–4068 (2013)
Xing, E.P., Karp, R.M.: CLIFF: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics 17, S306–S315 (2001)
Hansen, P., Ruiz, M., Aloise, D.: A VNS heuristic for escaping local extrema entrapment in normalized cut clustering. Pattern Recognit. 45(12), 4337–4345 (2012)
Kong, W., Hu, S., Zhang, J., Dai, G.: Robust and smart spectral clustering from normalized cut. Neural Comput. Appl. 23(5), 1503–1512 (2013)
Yanzhi, C., Yongfeng, H.: An experiment of medical image segmentation based on Ncut. In: 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2009). 5162320 (2009)
Fei, W., Lv, H., Wei, Z.: Satellite cloud image segmentation based on the improved normalized cuts model. In: 2009 1st International Conference on Information Science and Engineering (ICISE 2009). 5454832, pp. 1418–1421 (2009)
Cai, W., Chung, A.C.: Multi-resolution vessel segmentation using normalized cuts in retinal images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 9, pp. 928–936 (2006)
Kayal, D., Banerjee, S.: An approach to detect hard exudates using normalized cut image segmentation technique in digital retinal fundus image. Adv. Intell. Soft Comput. 166 AISC. 1, 123–128 (2012)
Yin, J., Sun, H., Yang, J., Guo, Q.: Automated detection of the arterial input function using normalized cut clustering to determine cerebral perfusion by dynamic susceptibility contrast-magnetic resonance imaging. J Magn Reson Imaging. (2014) Article in Press
Ghanem, B., Ahuja, N.: Dinkelbach NCUT: an efficient framework for solving normalized cuts problems with priors and convex constraints. Int. J. Comput. Vis. 89(1), 40–55 (2010)
Hochbaum, D.S.: Polynomial time algorithms for ratio regions and a variant of normalized cut. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 32(5), 889–898 (2010)
Gupta, A., Prasad, V.S.N., Davis, L.S.: Extracting regions of symmetry. In: Proceedings of the International Conference on Image Processing (ICIP 3). 1530346, pp. 133–136 (2005)
Fabijanska, A.: Normalized cuts and watersheds for image segmentation. In: IET Conference Publications (600 CP). (2012)
Tao, W., Jin, H., Zhang, Y.: Color image segmentation based on mean shift and normalized cuts. IEEE Trans. Syst. Man Cybern. 37(5), 1382–1389 (2007)
Geng, Y., Chen, J., Wang, L.: A novel color image segmentation algorithm based on JSEG and Normalized Cuts. In: Proceedings of the 2013 6th International Congress on Image and Signal Processing, CISP 2013. 1, 6744057, pp. 550–554 (2013)
De Bock, J., De Smet, P., Philips, W.: Image segmentation using Watershed and normalized Cut. In: Proceedings of SPIE—The international Society for Optical Engineering, vol. 20, pp. 164–173 (2005)
Fowlkes C., Martin D., Malik J. The Berkeley Segmentation Dataset and Benchmark (BSDB). http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html. (2012)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 33(5), 898–916 (2011)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore, MD, USA (1996)
Asghar, A., Rao, N.I.: Semantics sensitive segmentation and annotation of natural images. In: SITIS 2008–Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems. 4725831, pp. 387–394 (2008)
CIE Technical Report: Industrial color difference evaluation, pp. 116–1995. CIE Publication, Central Bureau, Vienna (1995)
Song, T., Luo, R.: Testing color-difference formulae on comple images using a CRT monitor. In: Proceedings of the IS and T/SID Color Imaging Conference, pp. 44–48 (2000)
CIE Technical, Report: CIE 142–2001. Improvement to industrial colour-difference evaluation. CIE 142–2001, Central Bureau of the CIE, Vienna (2001)
Arbelaez, P.: Notes on the Evaluation Methodology. Precision-Recall Framework. http://www.cs.berkeley.edu/arbelaez/Notes.html
Hanbury, A., Marcotegui, B.: Morphological segmentation on learned boundaries. Image Vis. Comput. 27(4), 480–488 (2009)
Lennie, P., Zmura, M.D.: Mechanisms of color vision. Crit. Rev. Neurobiol. 3, 333–400 (1988)
Poirson, A.B., Wandell, B.A.: Pattern color separable pathways predict sensitivity to simple colored patterns. Vis. Res. 36, 515–526 (1996)
Zhang, X., Wandell, B.: A spatial extension of CIELAB for digital color image reproduction. J. Soc. Inf. Disp. 5(1), 61–63 (1997)
Johnson, G.M., Fairchild, M.D.: A top down description of S-CIELAB and CIEDE2000. Color Res. Appl. 28, 425–435 (2003)
Valencia, E., Milln, M.S., Color image quality in presentation software. Adv. Optical Technol. 417976 (2008)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), (1971)
Freixenet, J., Munoz, X., Raba, D., Marti, J., Cufi, X.: Yet another survey on image segmentation: region and boundary information integration. In: European Conference on Computer Vision (ECCV 2002). 2352/2002, pp. 21–25 (2002).
John, W., Allen, Y.Y.: Image segmentation benchmark indices package. http://www.eecs.berkeley.edu/yang/software/lossysegmentation/ (2007)
Sarifuddin, M., Missaoui, B.: A new perceptually uniform color space with associated color similarity measure for content-based image and video retrieval, pp. 3–7. In: Proceedings of the ACM SIGIR Workshop on Multimedia, Information Retrieval (2005)
Rangayyan, R.M., Acha, B., Serrano, C.: Color image processing with biomedical applications. SPIE Press, Bellingham (2011)
Sreedhar, J., Viswanadha Raju, S., Vinaya Babu, A.: Query processing for content based image retrieval. Int. J. Soft Comput. Eng. (IJSCE). 1(5) (2011)
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This work is supported by the project TEC2010-21619-C04-02, CICYT, Spain. A.S. is founded by the Consejería de Innovación, Ciencia y Empresa of Junta de Andalucía, Spain.
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Sáez, A., Serrano, C. & Acha, B. Normalized Cut optimization based on color perception findings. A comparative study. Machine Vision and Applications 25, 1813–1823 (2014). https://doi.org/10.1007/s00138-014-0631-4
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DOI: https://doi.org/10.1007/s00138-014-0631-4