No Access

Hybrid Color Segmentation Method Using a Customized Nonlinear Similarity Function

Graduate Program in Computer Science, Federal University of Santa Catarina, Image Processing and Computer Graphics Lab, National Research Institute on Digital Convergence, Federal University of Santa Catarina, Brazil

Search for more papers by this author

S. L. Mantelli Neto

Brazilian Institute for Space Research, Image Processing and Computer Graphics Lab, National Research Institute on Digital Convergence, Federal University of Santa Catarina, Brazil

Search for more papers by this author

A. von Wangenheim

Image Processing and Computer Graphics Lab, National Research Institute on Digital Convergence, Federal University of Santa Catarina, Brazil

Federal University of Parana, Department of Informatics – DINF, Brazil

Search for more papers by this author

A. C. Sobieranski

Federal University of Parana, Department of Informatics – DINF, Brazil

4 Vision Lab - University of Itajai Valley, Brazil

Image Processing and Computer Graphics Lab, National Research Institute on Digital Convergence, Federal University of Santa Catarina, Brazil

Search for more papers by this author

L. Coser

Federal University of Santa Catarina, Pos-Graduation Program in Science and Material Engineering – PGMat, Brazil

Image Processing and Computer Graphics Lab, National Research Institute on Digital Convergence, Federal University of Santa Catarina, Brazil

Search for more papers by this author

, and

E. Comunello

4 Vision Lab, University of Itajai Valley, Brazil

Image Processing and Computer Graphics Lab, National Research Institute on Digital Convergence, Federal University of Santa Catarina, Florianpolis, Santa Catarina, Brazil

Search for more papers by this author

https://doi.org/10.1142/S0219467814500053Cited by:2 (Source: Crossref)

Abstract

Image segmentation is a fundamental step in several image processing tasks. It is a process where an image is divided into its constituent regions guided by a similarity criterion. One very interesting image segmentation method is the color structure code (CSC), which combines simultaneously split-and-merge and region-growing techniques. In this paper, a segmentation approach based on the CSC method, weighted color structure code (WCSC), is proposed. This method is guided by a nonlinear discrimination function, where the user-inference is captured by the Polynomial Mahalanobis distance, prioritizing, during the merging process, the regions with higher similarity to the user selected pattern. The WCSC has color distribution pattern-oriented characteristic, showing better coherence among the segments with higher similarity to the selected pattern. A qualitative evaluation and parametric paired analysis were performed to compare CSC, WCSC and other segmentation methods results, using images from Berkeley benchmark. The results from these comparison indicate an improvement on the segmentation result obtained by the WCSC.

Keywords:

Remember to check out the Check out our Most Cited Articles!
Check out these titles on Image Analysis

References

J. K. Udupaet al., Computerized Medical Imaging and Graphics 30, 75 (2006), DOI: 10.1016/j.compmedimag.2005.12.001. Crossref, Web of Science, Google Scholar
R. C. Gonzalez and R. E. Woods , Digital Image Processing , 3rd edn. ( Pearson International Edition , 2007 ) . Google Scholar
M. Sezgin and B. Sankur, Journal of Electronic Imaging 13, 146 (2004). Web of Science, Google Scholar
J. B. MacQueen, Some methods for classification and analysis of multivariate observations, Proc. 5th Berkeley Symp. Mathematical Statistics and Probability (1967) pp. 281–297. Google Scholar
D. Mumford and J. Shah, Comm. Pure Appl. Math. 42(5), 577 (1989), DOI: 10.1002/cpa.3160420503. Crossref, Web of Science, Google Scholar
L. Vincent and P. Soille, Watersheds in digital spaces: An efficient algorithm based on immersion simulations, CVRMed95: First Int. Conf. Computer Vision, Virtual reality and Robotics in Medicine9 (2009) pp. 735–744. Google Scholar
L. Priese and P. Sturm , Introduction to the Color Structure Code and Its Implementation , Institut fur Informatik, Universit at Koblenz-Landau ( 2003 ) . Google Scholar
A. Sobieranskiet al., Learning a nonlinear color distance metric for the identification of skin immunohistochemical staining, 22nd IEEE Int. Symp. Computer-Based Medical Systems, 2009. CBMS 2009 (2009) pp. 1–7. Google Scholar
Megawave Homepage (accessed 30 August 2013). URL http://megawave.cmla.ens-cachan.fr/ . Google Scholar
G. Mittelhäußer and F. Kruggel, Computer Vision, Virtual Reality and Robotics in Medicine, Lecture Notes in Computer Science 905, ed. N. Ayache (Springer, Berlin Heidelberg, 1995) pp. 237–241. Crossref, Google Scholar
L. Priese and P. Sturm, Color Structure Code source code, accessed on 30 August 2013. URL http://www.unikoblenz.de/~lb/lb_downloads/ . Google Scholar
A. von Wangenheimet al., Journal of the Brazilian Computer Society 14, 29 (2008), DOI: 10.1007/BF03192570. Crossref, Web of Science, Google Scholar
L. Priese and V. Rehrmann, A fast hybrid color segmentation method, Proc. Mustererkennung, DAGM Symposium (Springer-Verlag, 1993) pp. 297–304. Google Scholar
G. Dorkó , D. Paulus and U. Ahlrichs , Color segmentation for scene exploration , Workshop Farbbildverarbeitung ( Berlin, Germany , 2000 ) . Google Scholar
V. Rehrmann and L. Priese, Fast and robust segmentation of natural color scenes, Proc. 3rd Asian Conf. Computer Vision (Springer-Verlag, 1997) pp. 598–606. Google Scholar
S. Müller et al. , Automatic quality surveillance of GIS data with GEOAIDA , Proc. PIA 2003 — ISPRS Conf. Photogrammetric Image Analysis ( 2003 ) . Google Scholar
V. Rehrmann and M. Rothhaar, Detection and tracking of moving objects in color outdoor scenes, 30th ISATA: Dedicated Conference on Robotics, Motion and Machine Vision in the Automotive Industry (1997) pp. 16–19. Google Scholar
V. Rehrmann and D. Koblenz, Object oriented motion estimation in color image sequences, ECCV 19981406, LNCS (1997) pp. 704–719. Google Scholar
A. Al-Hamadi, R. Niese and B. Michaelis, Pattern Recognition and Image Analysis 17, 493 (2007), DOI: 10.1134/S1054661807040086. Crossref, Web of Science, Google Scholar
M. Ross, Segment cluster tracking, Conference on Colour in Graphics, Imaging and Vision2004 (2004) pp. 130–134. Google Scholar
L. Prieseet al., Traffic sign recognition based on color image evaluation, Proc. IEEE Intelligent Vehicles Symposium 93, IEEE (IEEE, 1993) pp. 95–100. Google Scholar
L. Prieseet al., New results on traffic sign recognition, Proceedings of the Intelligent Vehicles '94 Symposium (1994) pp. 249–254. Google Scholar
V. Rehrmann, R. Lakmann and L. Priese, A Parallel System for Realtime Traffic Sign Recognition (Image Rochester, NY, 1995) pp. 72–78. Google Scholar
L. Li and C. Tan, Graphics Recognition. Achievements, Challenges, and Evolution, Lecture Notes in Computer Science 6020, eds. J.-M. Ogier, W. Liu and J. Llads (Springer, Berlin/Heidelberg, 2010) pp. 175–186. Crossref, Google Scholar
M. Baikeret al., Automated registration of whole-body follow-up micro CT data of mice, Medical Image Computing and Computer-Assisted Intervention — MICCAI 2011, Lecture Notes in Computer Science, eds. G. Fichtinger, A. Martel and T. Peters (Springer Berlin, Heidelberg, 2011) pp. 516–523. Google Scholar
S. Lange and M. Riedmiller, Evolution of computer vision subsystems in robot navigation and image classification tasks, RoboCup 2004: Robot Soccer World Cup VIII3276, Lecture Notes in Computer Science, eds. D. Nardiet al. (Springer, Berlin/Heidelberg, 2005) pp. 184–195. Google Scholar
W. Xu and J. Mulligan, Advances in Visual Computing, Lecture Notes in Computer Science 5876, eds. G. Bebiset al. (Springer, Berlin/Heidelberg, 2009) pp. 264–273. Crossref, Google Scholar
J. Schmidt, I. Scholz and H. Niemann, Pattern Recognition, Lecture Notes in Computer Science 2191, eds. B. Radig and S. Florczyk (Springer, Berlin/Heidelberg, 2001) pp. 421–428. Crossref, Google Scholar
L. Priese, F. Schmitt and N. Hering, Image Analysis, Lecture Notes in Computer Science 5575, eds. A.-B. Salberg, J. Hardeberg and R. Jenssenm (Springer, Berlin/Heidelberg, 2009) pp. 726–734. Crossref, Google Scholar
H. Niemann, U. Ahlrichs and D. Paulus, Cognitive Vision Systems, Lecture Notes in Computer Science 3948, eds. H. Christensen and H.-H. Nagel (Springer, Berlin/Heidelberg, 2006) pp. 165–180. Crossref, Google Scholar
D. Stichling, B. Kleinjohann and K. Words, Design and Analysis of Distributed Embedded Systems (Kluwer Academic Publishers, 2002) pp. 247–256. Crossref, Google Scholar
G. Heidemann, Image and Vision Computing 26, 211 (2008), DOI: 10.1016/j.imavis.2007.05.001. Crossref, Web of Science, Google Scholar
A. Wangenheimet al., Pattern Recognition Letters 30, 1404 (2009), DOI: 10.1016/j.patrec.2009.07.005. Crossref, Web of Science, Google Scholar
I. E. Lutz , L. Priese and R. Schian , New Contrast Enhancement Filter for Automatic ( Institut fur Informatik , 1994 ) . Google Scholar
P. Sturm and L. Priese, Image Analysis, Lecture Notes in Computer Science 2749, eds. J. Bigun and T. Gustavsson (Springer, Berlin/Heidelberg, 2003) pp. 739–740. Google Scholar
P. Sturm, 3D-color-structure-code-segmentation by using a new nonplainness island hierarchy, Int. Conf. Image Processing, 2004, ICIP '04. 2004 (2004) pp. 953–956. Google Scholar
F. Schmitt, P. Sturm and L. Priese, Advances in Medical Engineering, Springer Proceedings in Physics 114, eds. T. M. Buzuget al. (Springer, Berlin, Heidelberg, 2007) pp. 187–192. Crossref, Google Scholar
P. Sturm and L. Priese, Pattern Recognition, Lecture Notes in Computer Science 2449, ed. L. Van Gool (Springer, Berlin/Heidelberg, 2002) pp. 274–281. Crossref, Google Scholar
L. Priese, P. Sturm and H. Wang, Image Analysis, Lecture Notes in Computer Science 3540, eds. H. Kalviainen, J. Parkkinen and A. Kaarna (Springer, Berlin/Heidelberg, 2005) pp. 6–16. Crossref, Google Scholar
NIST, Engineering statistics handbook, NIST — National Institute of Standards, 2009. URL http://www.itl.nist.gov/div898/handbook/eda/eda.htm . Google Scholar
J. B. Tenenbaum, C. Kemp and P. Shafto, Trends Cognitive Sci. 10, 309 (2006), DOI: 10.1016/j.tics.2006.05.009. Crossref, Web of Science, Google Scholar
A. Yuille and D. Kersten, Trends in Cognitive Neuroscience 10 (2006) pp. 301–308. Google Scholar
R. A. Johnson and D. A. Wichern , Applied Multivariate Staistical Analysis ( Pearson Education , 2007 ) . Google Scholar
G. Grudic, Outdoor path labeling using polynomial mahalanobis distance, Proc. Robotics: Science and Systems (2006) p. 4. Google Scholar
R. O. Duda , P. E. Hart and D. G. Stork , Pattern Classification ( John Wiley and Sons , 2001 ) . Google Scholar
G. Hartmann, Biological Cybernet. 57, 73 (1987), DOI: 10.1007/BF00318717. Crossref, Web of Science, Google Scholar
D. Martinet al., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Proc. 8th Int. Conf. Computer Vision2 (2001) pp. 416–423. Google Scholar
W. M. Rand, Journal of the American Statistical Association 66, 846 (1971), DOI: 10.1080/01621459.1971.10482356. Crossref, Web of Science, Google Scholar
A. C. Sobieranski, E. Comunello and A. von Wangenheim, Computer Vision and Image Understanding 115 (2011) pp. 127–139. Google Scholar
E. B. Fowlkes and C. L. Mallows, Journal of the American Statistical Association 78(383), 553 (1983), DOI: 10.1080/01621459.1983.10478008. Crossref, Web of Science, Google Scholar
A. Ben-Hur, A. Elisseeff and I. Guyon, A stability based method for discovering structure in clustered data, Pacific Symposium on Biocomputing (2002) pp. 6–17. Google Scholar
S. V. Dongen, "Performance criteria for graph clustering and Markov cluster experiments," Technical Report, National Research Institute for Mathematics and Computer Science (2000) . Google Scholar
J. W. Tukey , Exploratory Data Analysis ( Addison-Wesley , Reading, MA , 1977 ) . Google Scholar
D. C. Montgomery and G. C. Runger , Applied Statistics and Probability for Engineers ( John Wiley & Sons Inc , 2003 ) . Google Scholar
C. Christoudias, B. Georgescu and P. Meer, Synergism in low level vision, Proc. 16th Int. Conf. Pattern Recognition, 20024 (2002) pp. 150–155. Google Scholar
Y. Deng and B. Manjunath, IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 800 (2001), DOI: 10.1109/34.946985. Crossref, Web of Science, Google Scholar
, D-Dimensional formulation and implementation of recursive hierarchical segmentation , Disclosure of Invention and New Technology: NASA Case No GSC 15199-1 ( 2006 ) . Google Scholar