Gomez, D. and Montero de Juan, Francisco Javier and Yañez Gestoso, Javier (2006) A coloring fuzzy graph approach for image classification. Information Sciences, 176 (24). pp. 3645-3657. ISSN 0020-0255
Restricted to Repository staff only until 2020.
Official URL: http://www.sciencedirect.com/science/journal/00200255
One of the main problems in practice is the difficulty in dealing with membership functions. Many decision makers ask for a graphical representation to help them to visualize results. In this paper, we point out that some useful tools for fuzzy classification can be derived from fuzzy coloring procedures. In particular, we bring here a crisp grey coloring algorithm based upon a sequential application of a basic black and white binary coloring procedure, already introduced in a previous paper [D. Gomez, J. Montero, J. Yañez, C. Poidomani, A graph coloring algorithm approach for image segmentation, Omega, in press]. In this article, the image is conceived as a fuzzy graph defined on the set of pixels where fuzzy edges represent the distance between pixels. In this way,we can obtain a more flexible hierarchical structure of colors, which in turn should give useful hints about those classes with unclear boundaries.
|Uncontrolled Keywords:||Image classification; Decision making processes; Coloring problem|
|Subjects:||Sciences > Computer science > Artificial intelligence|
A. Amo, D. Gomez, J. Montero, G. Biging, Relevance and redundancy in fuzzy classification systems, Mathware and Soft Computing 8 (2001) 203–216.
A. Amo, J. Montero, V. Cutello, On the principles of fuzzy classification, in: R.N. Dave, T. Sudkamp (Eds.),Proceedings NAFIPS Conference, IEEE Press,Piscataway, NJ, 1999, pp.675–679.
A. Amo, J. Montero, G. Biging, Classifying pixels by means of fuzzy relations, International Journal on General Systems 29 (2000) 605–621.
A. Amo, J. Montero, G. Biging, V. Cutello, Fuzzy classification systems, European Journal of Operational Research 156 (2004) 459–507.
A. Amo, J. Montero, A. Fernandez, M. Lopez, J. Tordesillas, G. Biging, Spectral fuzzy classification: an application, IEEE Transactions on Systems Man and Cybernetics (C) 32 (2002) 42–48.
S. Bandyopadhyay, U. Maulik, An evolutionary technique based on k-means algorithm for optimal clustering in RN , Information Sciences 108 (1998) 219–240.
J.C. Bezdek, J.D. Harris, Fuzzy partitions and relations: an axiomatic basis for clustering,Fuzzy Sets and Systems 1 (1978) 111–127.
H.J. Caulfield, J. Fu, S. Yoo, Artificial color image logic, Information Sciences 167 (2004) 1–7.
H.D. Cheng, M. Miyojim, Automatic pavement distress detection system, Information Sciences 108 (1998) 219–240.
D. Dubois, H. Prade, Fuzzy Sets and Systems, Theory and Applications, Academic Press, New York, 1980.
D. Dubois, H. Prade, Ranking fuzzy numbers in the setting of possibility theory, Information Sciences 30 (1983) 183–224.
G. Facchinetti, R.G. Ricci, A characterization of a general class of ranking functions on triangular fuzzy numbers, Fuzzy Sets and Systems 146 (2004) 297–312.
D. Feng, S. Wenkang, C. Liangzhou, D. Yong, Z. Zhenfu, Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization,Pattern Recognition Letters 26 (2005) 597–603.
G.M. Foody, The continuum of classification fuzziness in thematics mapping, Photogrammetric Engineering and Remote Sensing 65 (1999) 443–451.
D. Gomez, J. Montero, J. Yañez, C. Poidomani, A graph coloring algorithm approach for image segmentation, Omega, in press.
|Deposited On:||10 Oct 2012 10:19|
|Last Modified:||10 Oct 2012 10:19|
Repository Staff Only: item control page