Abstract
This paper presents a new, simple, and efficient segmentation approach. Firstly, choose the best segmentation components among six different color spaces. Then, Histogram and SFCM techniques are applied for initialization of segmentation. Finally, fuse the segmentation results and merge similar regions. Extensive experiments have been taken on Berkeley image database by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, our method could achieve better image partitioning and better performance.
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© 2012 Springer-Verlag Berlin Heidelberg
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Chen, K., Ma, Y., Liu, J., Li, Sb. (2012). A Self-adaptive Segmentation Method by Fusion of Multi-color Space Components. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_47
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DOI: https://doi.org/10.1007/978-3-642-33478-8_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33477-1
Online ISBN: 978-3-642-33478-8
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