Abstract
Recent vehicle identification systems based on radio frequency identification (RFID) often suffer from the challenges including long distance limitation and risk of malevolent tampering. A natural idea is to integrate multiple visual features with RFID information to improve the identification performance. In this paper, we propose an improved visual feature representation method, called hyper-graph regularized multi-view matrix factorization (HMMF), for vehicle identification. The proposed HMMF pushes cross-view clusters towards a common embedding, and maintains the high-order within-view structure simultaneously. We further propose semi-supervised HMMF (SemiHMMF) to incorporate the labels to utilize the partial labels of RFID data. An iterative optimization algorithm is developed based on multiplicative rules. Experiments on two real-world datasets demonstrate the effectiveness of the proposed methods on vehicle identification.
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Acknowledgments
This paper is supported by national key research and development program of China (Grant No. 2017YFC0804806), national natural science foundation of China (Grant No. 61603159) and natural science foundation of Jiangsu province (Grant No. BK20160293).
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Qian, B., Shen, X., Shu, Z., Gu, X., Huang, J., Hu, J. (2018). Hyper-graph Regularized Multi-view Matrix Factorization for Vehicle Identification. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_50
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DOI: https://doi.org/10.1007/978-3-030-00006-6_50
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