Symmetric Subspace Learning for Image Analysis

Konstantinos Papachristou, Anastasios Tefas, Ioannis Pitas

Research output: Contribution to journalArticle (Academic Journal)peer-review

11 Citations (Scopus)
288 Downloads (Pure)

Abstract

Subspace Learning is one of the most useful tools for image analysis and recognition. A large number of such techniques has been proposed utilizing a-priori knowledge about the data. In this paper, new Subspace Learning techniques are presented that use symmetry constraints in their objective functions. The rational behind this idea is to exploit the apriori knowledge that geometrical symmetry appears in several types of data, such as images, objects, faces, etc. Experiments on artificial, facial expression recognition, face recognition and object categorization databases highlight the superiority and the robustness of the proposed techniques, in comparison to standard Subspace Learning techniques.
Original languageEnglish
Pages (from-to)5683-5697
Number of pages15
JournalIEEE Transactions on Image Processing
Volume23
Issue number12
DOIs
Publication statusPublished - 5 Nov 2014

Keywords

  • Subspace learning
  • symmetry constraints
  • principal component analysis (PCA)
  • Linear Discriminant analysis (LDA)
  • clustering based discriminant analysis (CDA)

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