Sparse discriminative multi manifold embedding based on graph optimization

Yang Yang; Zhengqun Wang; Chunlin Xu; Chen Yan

doi:10.1117/12.2524190

6 May 2019 Sparse discriminative multi manifold embedding based on graph optimization

Yang Yang, Zhengqun Wang, Chunlin Xu, Chen Yan

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690M (2019) https://doi.org/10.1117/12.2524190
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Sparse discriminative multi manifold embedding (SDMME) algorithm was used for feature extraction, graph construction and projection learning were independent, the quality of the graph directly affects the effect of projection learning. In order to solve the problem, a new algorithm named sparse discriminative multi manifold embedding based on graph optimization (GOSDMME) was proposed in this paper. First, in proposed approach, the image matrix was divided into blocks. The matrix blocks on the same image were located on the same manifold. Then, the sparse graph was used to establish the connection relationship between different blocks. Finally, in the framework of the same objective function, the sparse constraint graphs and projections were studied simultaneously. The graphs and projections were learned at the same time, iterate and update the graph and projection to obtain a projection matrix that satisfies the accuracy requirements. The face recognition experiments conducted on Extended Yale B and CMU PIE datasets show that the new algorithm has better recognition performance than the SDMME algorithm.

Citation Download Citation

Yang Yang, Zhengqun Wang, Chunlin Xu, and Chen Yan "Sparse discriminative multi manifold embedding based on graph optimization", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690M (6 May 2019); https://doi.org/10.1117/12.2524190

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KEYWORDS

Associative arrays

Facial recognition systems

Detection and tracking algorithms

Optimization (mathematics)

Feature extraction

Principal component analysis

Laser applications

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