Abstract
Color image segmentation is an important research topic in the field of computer vision. In this paper, we propose a method for image segmentation by computing similarity coefficient in RGB color space. Then, we apply the density-based clustering algorithm TI-DBSCAN on regions growing rules that in turn speeds up the process. This new method has three advantages. First, this method can reduce the disturbance of noise and get the segmentation numbers more accurately. Second, it needn’t to change the RGB color space to other space. Third, it uses a triangle inequality property to quickly reduce the neighborhood search space. The experimental results illustrate that the new approach segmentation method can efficiently segment image.
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References
Chen, C.W., Luo, J., Parker, K.J.: IEEE Trans. on Image Processing 7(12), 1673–1683 (1998)
Parvati, K., Prakasa, R.S., Mariya, D.M.: Discrete Dynamics in Nature and Society, pp. 1–8 (2008)
Otman, B., Hongwei, Z., Fakhri, K.: Fuzzy Based Image Segmentation. Springer, Berlin (2003)
Lu, H., Zhang, L., Serikawa, S., et al.: A Method for Infrared Image Segment Based on Sharp Frequency Localized Contourlet Transform and Morphology. In: ICICIP 2010, pp. 79–82 (2010)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Publishing House of Electronics Industry, Beijing (2004)
Han, J., Kamber, M.: Data mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Hung, M., Yang, D.: An Efficient Fuzzy C-Means Clustering Algorithm. In: IEEE Intel. Conf. on Data Mining, pp. 225–232 (2001)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: A Density-based Algorithm for Discovering Spatial Databases With Noise. In: Proc. of 2th ICKDDM (1996)
Kryszkiewicz, M., Lasek, P.: TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality. LNCS. Springer, Berlin (2010)
Han, X., Li, J., et al.: An Approach of Color Object Searching for Vision System of Soccer Robot. In: Proc. ICRB, pp. 535–539 (2004)
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Li, Y., Lu, H., Zhang, L., Yang, S., Serikawa, S. (2012). Color Image Segmentation Using Fast Density-Based Clustering Method. In: Zhang, Y. (eds) Future Communication, Computing, Control and Management. Lecture Notes in Electrical Engineering, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27311-7_79
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DOI: https://doi.org/10.1007/978-3-642-27311-7_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27310-0
Online ISBN: 978-3-642-27311-7
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