Semantic facial description via axiomatic Fuzzy Set based clustering
In this paper, we developed a new method to extract semantic face descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. First we used the landmark-based geometry features to represent facial components, and then developed a new feature selection algorithm to select some salie...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/33673 |
| _version_ | 1848754012685336576 |
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| author | Li, Q. Ren, Y. Liu, Wan-Quan Li, Ling |
| author_facet | Li, Q. Ren, Y. Liu, Wan-Quan Li, Ling |
| author_sort | Li, Q. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we developed a new method to extract semantic face descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. First we used the landmark-based geometry features to represent facial components, and then developed a new feature selection algorithm to select some salient features based on dissimilarity 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 | 2025-11-14T08:33:39Z |
| format | Conference Paper |
| id | curtin-20.500.11937-33673 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:33:39Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-336732017-09-13T15:32:48Z Semantic facial description via axiomatic Fuzzy Set based clustering Li, Q. Ren, Y. Liu, Wan-Quan Li, Ling In this paper, we developed a new method to extract semantic face descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. First we used the landmark-based geometry features to represent facial components, and then developed a new feature selection algorithm to select some salient features based on dissimilarity 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 Conference Paper http://hdl.handle.net/20.500.11937/33673 10.1109/FSKD.2015.7382037 restricted |
| spellingShingle | Li, Q. Ren, Y. Liu, Wan-Quan Li, Ling Semantic facial description via axiomatic Fuzzy Set based clustering |
| title | Semantic facial description via axiomatic Fuzzy Set based clustering |
| title_full | Semantic facial description via axiomatic Fuzzy Set based clustering |
| title_fullStr | Semantic facial description via axiomatic Fuzzy Set based clustering |
| title_full_unstemmed | Semantic facial description via axiomatic Fuzzy Set based clustering |
| title_short | Semantic facial description via axiomatic Fuzzy Set based clustering |
| title_sort | semantic facial description via axiomatic fuzzy set based clustering |
| url | http://hdl.handle.net/20.500.11937/33673 |