Abstract
We present a color image segmentation algorithm, RCRM, based on the detection of Representative Colors and on Region Merging. The 3D color histogram of the RGB input image is built. Colors are processed in decreasing frequency order and a grouping process is accomplished to gather in the same cluster all colors that are close enough to the current color. Colormapping is done to originate a preliminary image segmentation. Segmentation regions having small size undergo a merging process. Merging is actually accomplished only for adjacent regions whose colors do not significantly differ. The parameters involved by the algorithm are set automatically by taking into account color distribution in the input image and geometrical features of the regions into which the image is partitioned. The algorithm has been tested on a large number of RGB color images originating satisfactory results.
Chapter PDF
Similar content being viewed by others
References
Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)
Busin, L., Vandenbroucke, N., Macaire, L.: Color spaces and image segmentation. In: Advances in Imaging and Electron Physics, ch. 2, JCR Science Edition, Orlando, FL, USA, vol. 1, pp. 65–168 (2008)
Bhattacharyya, S.: A Brief Survey of Color Image Preprocessing and Segmentation Techniques. J. of Pattern Recognition Research 1, 120–129 (2011)
Ramella, G., Sanniti di Baja, G.: Color histogram-based image segmentation. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part I. LNCS, vol. 6854, pp. 76–83. Springer, Heidelberg (2011)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Information Theory 28(2), 129–136 (1982)
Berkhin, P.: Survey of clustering data mining techniques. Accrue Software, San Jose, CA. Technical Report (2002)
Zhang, H., Fritts, J., Goldman, S.: An entropy-based objective evaluation method for image segmentation. In: Proceedings of SPIE 5307 Storage and Retrieval Methods and Applications for Multimedia, pp. 38–49 (2004)
Rehrmann, V., Priese, L.: Fast and robust segmentation of natural color scenes. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1351, pp. 598–606. Springer, Heidelberg (1997)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pattern Anal. Machine Intell. 24(5), 603–619 (2002)
Tilton, J.: D-dimensional formulation and implementation of recursive hierarchical segmentation, Disclosure of Invention and New Technology: NASA Case No. GSC 15199-1 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramella, G., Sanniti di Baja, G. (2013). Image Segmentation Based on Representative Colors Detection and Region Merging. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds) Pattern Recognition. MCPR 2013. Lecture Notes in Computer Science, vol 7914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38989-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-38989-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38988-7
Online ISBN: 978-3-642-38989-4
eBook Packages: Computer ScienceComputer Science (R0)