Abstract
This paper addresses implementation of on-line trained neural network for fast color image segmentation. A pre-selecting technique, based on mean shift algorithm and uniform sampling, is utilized as an initialization tool to largely reduce the training set while preserving the most valuable distribution information. Furthermore, we adopt Particle Swarm Optimization (PSO) to train neural network for a faster convergence and escaping from a local optimum. The results obtained from a wide range of color blood cell images show that under the compatible image segmentation performance on the test set, the training set and running time can be reduced significantly, compared with traditional training methods.
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Fang, Y., Pan, C., Liu, L. (2005). On-line Training of Neural Network for Color Image Segmentation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_22
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DOI: https://doi.org/10.1007/11539117_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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