10 April 2018 Multi scales based sparse matrix spectral clustering image segmentation
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061527 (2018) https://doi.org/10.1117/12.2302812
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.
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Zhongmin Liu, Zhicai Chen, Zhanming Li, Wenjin Hu, "Multi scales based sparse matrix spectral clustering image segmentation", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061527 (10 April 2018); doi: 10.1117/12.2302812; https://doi.org/10.1117/12.2302812
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