Spectral clustering based on high‐frequency texture components for face datasets
Abstract Spectral clustering is one of the most widely used technologies for clustering tasks, which represents data as a weighted graph, and aims to find an appropriate way to cut the graph apart in order to categorize the raw data. The pivotal step of spectral clustering is to find out the accurat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-08-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12191 |
| Summary: | Abstract Spectral clustering is one of the most widely used technologies for clustering tasks, which represents data as a weighted graph, and aims to find an appropriate way to cut the graph apart in order to categorize the raw data. The pivotal step of spectral clustering is to find out the accurate information to estimate the relationship of pairwise data, based on which a graph can be constructed. According to the cognition that different faces are distinguished by the edge contour which can be represented by high‐frequency texture components, a novel spectral clustering algorithm via high‐frequency signal of human face, named high‐frequency spectral clustering (HFSC) is proposed. In HFSC, first the local high‐frequency texture components are extracted from samples. Then the relationship of pairwise samples can be estimated with the degree of correlation, which is produced from the image high‐frequency information. The graph will be set up with the correlation information. Subsequently the graph cut will be implemented to achieve the final clustering results. Experimental results show that this algorithm outperforms the state‐of‐the‐art clustering methods on several datasets. |
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| ISSN: | 1751-9659 1751-9667 |