Abstract
In this work we carry out a comparison study between different color spaces in clustering-based image segmentation. We use two similar clustering algorithms, one based on the entropy and the other on the ignorance. The study involves four color spaces and, in all cases, each pixel is represented by the values of the color channels in that space. Our purpose is to identify the best color representation, if there is any, when using this kind of clustering algorithms.
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References
Alata, O., Quintard, L.: Is there a best color space for color image characterization or representation based on Multivariate Gaussian Mixture Model? Computer Vision and Image Understanding 113, 867–877 (2009)
Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R.: Fuzzy Models and algorithms for pattern recognition and image processing. In: Dubois, D., Prade, H. (Series eds.). The Handbooks of Fuzzy Sets Series. Kluwer Academic Publishers, Dordrecht (1999)
Bustince, H., Pagola, M., Barrenechea, E.: Construction of fuzzy indices from fuzzy DI-subsethood measures: Application to the global comparison of images. Information Sciences 177, 906–929 (2007)
Bustince, H., Pagola, M., Barrenechea, E., Fernandez, J., Melo-Pinto, P., Couto, P., Tizhoosh, H.R., Montero, J.: Ignorance functions. An application to the calculation of the threshold in prostate ultrasound images. Fuzzy Sets and Systems 161(1), 20–36 (2010)
Celenk, M.: A Color Clustering Technique for Image Segmentation. Computer Vision Graphics and Image Processing 52(2), 145–170 (1990)
Chaves-González, J.M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Detecting skin in face recognition systems: A colour spaces study. Digital Signal Process. (2009), doi:10.1016/j.dsp.2009.10.008
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34(12), 2259–2281 (2001)
Du, C.-J., Sun, D.-W.: Comparison of three methods for classification of pizza topping using different colour space transformations. Journal of Food Engineering 68, 277–287 (2005)
Lo, H., Am, B., Lp, C., et al.: A Comparison of Neural Network and Fuzzy Clustering-Techniques in Segmenting Magnetic-Resonance Images of the Brain. IEEE Transactions on Neural Networks 3(5), 672–682 (1992)
Jurio, A., Pagola, M., Paternain, D., Barrenechea, E., Sanz, J., Bustince, H.: Ignorance-based fuzzy clustering algorithm. In: Ninth International Conference on Intelligent Systems Design and Applications, pp. 1353–1358 (2009)
Jurio, A., Pagola, M., Paternain, D., Lopez-Molina, C., Melo-Pinto, P.: Interval-valued restricted equivalence functions applied on Clustering Techniques. In: 13rd International Fuzzy Systems Association World Congress and 6th European Society for Fuzzy Logic and Technology Conference (IFSA-EUSFLAT 2009) (2009)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database o f human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l. Conf. Computer Vision, July 2001, vol. 2, pp. 416–423 (2001)
Nam, I., Salamah, S., Ngah, U.: Adaptive Fuzzy Moving K-Means Clustering Algorithm For Image Segmentation. IEEE Transactions on Consumer Electronics 55(4), 2145–2153 (2009)
Pal, N.R., Pal, S.K.: A review of image segmentation techniques. Pattern recognition 26, 1277–1294 (1993)
Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., Aparicio-Tejo, P., Lamsfus, C., Lasa, B.: New method to assess barley nitrogen nutrition status based on image colour analysis: Comparison with SPAD-502. Computers and Electronics in Agriculture 65(2), 213–218 (2009)
Ruiz-Ruiz, G., Gómez-Gil, J., Navas-Gracia, L.M.: Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Computers and Electronics in Agriculture 68(1), 88–96 (2009)
Vandenbroucke, N., Macaire, L., Postaire, J.G.: Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis. Computer Vision and Image Understanding 90(2), 190–216 (2003)
Yao, J., Dash, M., Tan, S.T., Liu, H.: Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets Syst. 113(3), 381–388 (2000)
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Jurio, A., Pagola, M., Galar, M., Lopez-Molina, C., Paternain, D. (2010). A Comparison Study of Different Color Spaces in Clustering Based Image Segmentation. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_55
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DOI: https://doi.org/10.1007/978-3-642-14058-7_55
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