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Proceedings Paper

Joint concept factorization for image representation
Author(s): Xianzhong Long
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Paper Abstract

As one kind of popular clustering techniques, Concept Factorization (CF) has been widely employed in computer vision and pattern recognition fields. However, existing clustering algorithms based on CF do not consider the complementarity between multiple features. In order to solve this problem, many joint learning methods have been proposed in recent years, such as Joint Non-negative Matrix Factorization (JNMF), Laplacian Regularized Joint Non-negative Matrix Factorization (LJ-NMF). Inspired by these, Joint Concept Factorization (JCF) and Joint Locally Consistent Concept Factorization (JLCCF) schemes are proposed in this paper. Experimental results on image clustering show that the proposed schemes outperform some existing algorithms in terms of accuracy and normalized mutual information.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064R (9 August 2018); doi: 10.1117/12.2503135
Show Author Affiliations
Xianzhong Long, Nanjing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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