From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach
In this paper, we developed a new method to extract semantic facial descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. Landmark-based geometry features are first used to represent facial components, and then we developed a new feature selection algorithm to select salient...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IOS PRESS
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/41933 |
| _version_ | 1848756280432263168 |
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| author | Li, Q. Ren, Y. Li, L. Liu, Wan-Quan |
| author_facet | Li, Q. Ren, Y. Li, L. Liu, Wan-Quan |
| author_sort | Li, Q. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we developed a new method to extract semantic facial descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. Landmark-based geometry features are first used to represent facial components, and then we developed a new feature selection algorithm to select salient features based on feature similarities defined in AFS. Finally, the AFS-based clustering technique was used to extract the high-level semantic concepts. Extensive experiments showed that the proposed method can achieve much better results than the conventional clustering approaches like K-means and Fuzzy c-means clustering (FCM). |
| first_indexed | 2025-11-14T09:09:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-41933 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:09:41Z |
| publishDate | 2016 |
| publisher | IOS PRESS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-419332017-09-13T14:19:38Z From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach Li, Q. Ren, Y. Li, L. Liu, Wan-Quan In this paper, we developed a new method to extract semantic facial descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. Landmark-based geometry features are first used to represent facial components, and then we developed a new feature selection algorithm to select salient features based on feature similarities defined in AFS. Finally, the AFS-based clustering technique was used to extract the high-level semantic concepts. Extensive experiments showed that the proposed method can achieve much better results than the conventional clustering approaches like K-means and Fuzzy c-means clustering (FCM). 2016 Journal Article http://hdl.handle.net/20.500.11937/41933 10.3233/JIFS-169009 IOS PRESS restricted |
| spellingShingle | Li, Q. Ren, Y. Li, L. Liu, Wan-Quan From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach |
| title | From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach |
| title_full | From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach |
| title_fullStr | From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach |
| title_full_unstemmed | From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach |
| title_short | From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach |
| title_sort | from low-level geometric features to high-level semantics: an axiomatic fuzzy set clustering approach |
| url | http://hdl.handle.net/20.500.11937/41933 |