From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach

Issue title: ICNC-FSKD 2015

Guest editors: Zheng Xiao and Kenli Li

Article type: Research Article

Authors: Li, Qilin^{a; *} | Ren, Yan^b | Li, Ling^a | Liu, Wanquan^a

Affiliations: [a] Department of Computing, Curtin University, Perth, WA, Australia | [b] College of Automation, Shenyang Aerospace University, Shenyang, P.R. China

Correspondence: [*] Corresponding author. Qilin Li, Department of Computing, Curtin University, Perth, WA, Australia. Tel.: +61 416499977; E-mails: [email protected], [email protected].

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).

Keywords: Face representation, semantic description, AFS learning, feature selection, fuzzy clustering

DOI: 10.3233/JIFS-169009

Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 775-786, 2016