Joint concept factorization for image representation

Xianzhong Long

doi:10.1117/12.2503135

9 August 2018 Joint concept factorization for image representation

Xianzhong Long

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108064R (2018) https://doi.org/10.1117/12.2503135
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Xianzhong Long "Joint concept factorization for image representation", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064R (9 August 2018); https://doi.org/10.1117/12.2503135

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