Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition

• Published in: Applied Sciences
• Main Authors: Xizhan Gao, Kang Wei, Jia Li, Ziyu Shi, Hui Zhao, Sijie Niu
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: set-based video recognition; image set classification; manifold learning; fast and accurate classification; discriminative dictionary learning; Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Biology (General); QH301-705.5; Physics; QC1-999; Chemistry; QD1-999; DML
• Online Access: https://doi.org/10.3390/app13116383; https://doaj.org/article/8e0f365212444a8a91a8edd2cfffd676

As an important research direction in image and video processing, set-based video recognition requires speed and accuracy. However, the existing static modeling methods focus on computational speed but ignore accuracy, whereas the dynamic modeling methods are higher-accuracy but ignore the computational speed. Combining these two types of methods to obtain fast and accurate recognition results remains a challenging problem. Motivated by this, in this study, a novel Manifolds-based Low-Rank Dictionary Pair Learning (MbLRDPL) method was developed for a set-based video recognition/image set classification task. Specifically, each video or image set was first modeled as a covariance matrix or linear subspace, which can be seen as a point on a Riemannian manifold. Second, the proposed MbLRDPL learned discriminative class-specific synthesis and analysis dictionaries by clearly imposing the nuclear norm on the synthesis dictionaries. The experimental results show that our method achieved the best classification accuracy (100%, 72.16%, 95%) on three datasets with the fastest computing time, reducing the errors of state-of-the-art methods (JMLC, DML, CEBSR) by 0.96–75.69%.