Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Eyebrow semantic description via clustering based on Axiomatic Fuzzy Set

    Access Status
    Fulltext not available
    Authors
    Li, D.
    Ren, Y.
    Du, T.
    Liu, Wan-Quan
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, D. and Ren, Y. and Du, T. and Liu, W. 2018. Eyebrow semantic description via clustering based on Axiomatic Fuzzy Set. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8 (6): Article ID e1275.
    Source Title
    Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
    DOI
    10.1002/widm.1275
    ISSN
    1942-4787
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/73420
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we aim to extract the eyebrow semantic descriptors based on the Axiomatic Fuzzy Set (AFS) theory. First, we normalize the image of the eyebrows and automatically mark it by using a recently proposed facial landmarks detector. Second, a recent clustering algorithm based on AFS theory for eyes semantics abstraction is used to cluster these detected eyebrow landmarks and give semantic descriptors for each eyebrow. Finally, BU-4DFE and Multi-PIE databases are used to validate the effectiveness of the proposed approach. Furthermore, the eyebrow descriptions with different expressions and similar expressions are investigated and we show that the semantic descriptors are closely related to expressions. The experimental results show that the eyebrow semantic concepts obtained by the AFS clustering algorithm are better than the results produced by the traditional clustering methods (k-means and FCM) in terms of consistency for different expressions. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Algorithmic Development > Biological Data Mining.

    Related items

    Showing items related by title, author, creator and subject.

    • Person-independent facial expression recognition via hierarchical classification
      Xue, Mingliang; Liu, Wan-Quan; Li, Ling (2013)
      Automatically recognizing facial expressions presents an active and challenging problem in computer vision and pattern classification. The person-independent case is even more challenging. In this paper, we propose a ...
    • Multi-ethnic facial features extraction based on axiomatic fuzzy set theory
      Li, Z.; Duan, X.; Zhang, Q.; Wang, C.; Wang, Y.; Liu, Wan-Quan (2017)
      This paper proposes a new semantic concept extraction method to choose the salient features for representing multi-ethnic face characteristics based on axiomatic fuzzy set (AFS) theory. It has two advantages, one is that ...
    • Improving the relevance of web search results by combining web snippet categorization, clustering and personalization
      Zhu, Dengya (2010)
      Web search results are far from perfect due to the polysemous and synonymous characteristics of nature languages, information overload as the results of information explosion on the Web, and the flat list, “one size fits ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.