Kwis

Only available on StudyMode
  • Topic: Color, Cluster analysis, Image processing
  • Pages : 21 (6085 words )
  • Download(s) : 68
  • Published : March 14, 2013
Open Document
Text Preview
ÓPTICA PURA Y APLICADA. www.sedoptica.es

Color Space Analysis for Color Image Segmentation of Natural Scenes by K-means Based Clustering Jia Song(1,∗) , Eva M. Valero(1) , Juan L. Nieves(1)
1. Optics Department, Faculty of Science, University of Granada, Spain (∗)

Corresponding author Email: songjia815@gmail.com
Recibido / Received: dd/mm/yyyy Aceptado / Accepted: dd/mm/yyyy

ABSTRACT: In this paper, we propose to segment color images of natural scenes by pixel clustering in different color spaces in order to compare the performances of the color spaces. Eight color spaces are evaluated, including perceptual and non-perceptual color spaces. The clustering methods applied in this work are k-means and its extensions. Segmentation results are evaluated by calculating the degree of matching with the benchmark created by manual labeling, and visual evaluation is also applied as a reference. A series of experiments were designed and conducted. Results show that the performances of the color spaces are dependent on the scenes, and it is possible to choose a proper color space manually for a specific image scene segmentation task according to the data dependency patterns analyzed from the experiment results. Key words: color image segmentation, natural scenes, color spaces, k-means clustering

References
[1] Busin, L., Vandenbroucke, N., and Macaire, L., "Color spaces and image segmentation", Advances in Imaging and Electron Physics 151, 65–168 (2008). [2] Cheng, H., Jiang, X., Sun, Y., and Wang, J., "Color image segmentation: advances and prospects", Pattern Recognition 34(12), 2259–2281 (2001). [3] Busin, L., Shi, J., Vandenbroucke, N., and Macaire, L., "Color space selection for color image segmentation by spectral clustering", IEEE International Conference on Signal and Image Processing Applications 22(8), 262–267 (2009). [4] Fernando E. Correa-Tome, Raul E. Sanchez-Yanez and Victor Ayala-Ramirez, "Comparison of perceptual color spaces for natural image segmentation tasks", Opt. Eng 50, 117–203 (2011). [5] Ford, A. and Roberts, A., "Colour space conversions", Westminster University, London (1998). [6] N. Vandenbroucke, L. Macaire, and J.-G. Postaire, "Color image segmentation by pixel classification in an adapted hybrid color space. Application to soccer image analysis", Computer Vision and Image Understanding 90, 190–216 (2003). [7] Smith, T. and Guild, J., "The cie colorimetric standards and their use", Transactions of the Optical Society 33, 73 (1931). [8] Ohta, Y. I., Kanade, T., Sakai, T., "Color information for region segmentation", Computer Graphics and Image Processing 13, 222–241 (1980). [9] Wang, H. and Suter, D., "Color image segmentation using global information and lo- cal homogeneity", In Proceeding of 7th Conf. of Digital Image Computing: Techniques and Applications, (2003).

Opt. Pura Apl. NN (A) XXX-XXX (YYYY)

-0-

c Sociedad Española de Óptica

ÓPTICA PURA Y APLICADA. www.sedoptica.es

[10] Grana, C., Vezzani, R., Cucchiara, R., "Enhancing (H, S, V) Histograms with Achromatic Points Detection for Video Retrieval", Proceedings of the 6th ACM International Conference on Image and Video Retrieval, 302–308 (2007). [11] Steinhaus, H., "Sur la division des corps matériels en parties", Bull. Acad. Polon. Sci, 801–804 (1957). [12] Jain, A., "Data clustering: 50 years beyond k-means", Pattern Recognition Letters 31(8), 651–666 (2010). [13] Balasko, B., Abonyi, J., and Feil, B., "Fuzzy clustering and data analysis toolbox", Department of Process Engineering University of Veszprem, Veszprem, Hungary., (2005). [14] Bezdek, J., "Pattern Recognition with Fuzzy Objective Function Algorithms", Kluwer Academic Publishers (1981). [15] Babuka, R., Van der Veen, P., and Kaymak, U., "Improved covariance estimation for Gustafson Kessel clustering", IEEE International Conference on Fuzzy Systems 2, 1081–1085 (2002). [16] Gath, I. and Geva, A., "Unsupervised Optimal Fuzzy Clustering", IEEE Transactions on...
tracking img