Research on spectral clustering infrared image segmentation algorithm based on improved sparse matrix

Xiaofeng Zhao; Yinpeng Wei; Wei Cai; Changqing Liu

doi:10.1117/12.2503032

9 August 2018 Research on spectral clustering infrared image segmentation algorithm based on improved sparse matrix

Xiaofeng Zhao, Yinpeng Wei, Wei Cai, Changqing Liu

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062U (2018) https://doi.org/10.1117/12.2503032
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

In the infrared image segmentation, spectral clustering needs to calculate the similarity matrix between pixel points, the amount of data is large and the calculation is time-consuming. To solve this problem, an improved spectral clustering infrared image segmentation algorithm based on improved sparse matrix is proposed. The algorithm combines the feature of the whole image with the relationship between pixels, and then convinces the network to extract the infrared image feature information through convolution, and uses the selected feature information to construct the sparse similarity matrix, and completes the segmentation by combining the spectral clustering method. Experimental results show that this algorithm can effectively reduce the computational complexity of spectral clustering and effectively improve the segmentation result of the target area of infrared images.

Citation Download Citation

Xiaofeng Zhao, Yinpeng Wei, Wei Cai, and Changqing Liu "Research on spectral clustering infrared image segmentation algorithm based on improved sparse matrix", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062U (9 August 2018); https://doi.org/10.1117/12.2503032

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