From low-level geometric features to high-level semantics: : An axiomatic fuzzy set clustering approach
Pages 775 - 786
Abstract
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).
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- From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach
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Published: 01 January 2016
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