Abstract
In this paper, a new clustering approach by simulating human vision process is presented. Human is good at detecting and segmenting objects from the background, even when these objects have not been seen before, which are clustering activities in fact. Since human vision shows good potential in clustering, it inspires us that reproducing the mechanism of human vision may be a good way of data clustering. Following this idea, we present a new clustering approach by reproducing the three functional levels of human vision. Numeric examples show that our approach is feasible, computationally stable, suitable to discover arbitrarily shaped clusters, and insensitive to noises.
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Acknowledgments
This paper is sponsored by the Scientific Research Foundation of Guangxi University (Grant No. XBZ120366) and supported by NSFC, Tian Yuan Special Foundation, Project No. 11226141.
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© 2013 Springer-Verlag Berlin Heidelberg
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Jin, D., Huang, Z. (2013). A New Vision Inspired Clustering Approach. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_15
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DOI: https://doi.org/10.1007/978-3-642-38466-0_15
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Online ISBN: 978-3-642-38466-0
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