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Symmetry 2018, 10(7), 272; https://doi.org/10.3390/sym10070272

Incremental Spectral Clustering via Fastfood Features and Its Application to Stream Image Segmentation

1
Department of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
*
Author to whom correspondence should be addressed.
Received: 21 May 2018 / Revised: 6 July 2018 / Accepted: 9 July 2018 / Published: 11 July 2018
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Abstract

We propose an incremental spectral clustering method for stream data clustering and apply it to stream image segmentation. The main idea in our work consists of generating the data points in the kernel space by Fastfood features and iteratively calculating the eigendecomposition of data. Compared with the popular Nyström-based approximation, our work accesses each data point only once while Nyström, in particular the sampling scheme, will go through the entire dataset first and calculate the embeddings of data points with a second visit. As a result, our method is able to learn data partitions incrementally and improve eigenvector approximation with more and more data seen from a stream. By contrast, the performance of the standard Nyström is fixed when the sample set is selected. Experimental results show the superiority of our method. View Full-Text
Keywords: symmetric kernel matrix; spectral clustering; fastfood features; Nyström approximation; pattern recognition symmetric kernel matrix; spectral clustering; fastfood features; Nyström approximation; pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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He, L.; Li, Y.; Zhang, X.; Chen, C.; Zhu, L.; Leng, C. Incremental Spectral Clustering via Fastfood Features and Its Application to Stream Image Segmentation. Symmetry 2018, 10, 272.

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