Simultaneous Feature and Sample Reduction for Image-Set Classification

Image-set classification is the assignment of a label to a given image set. In real-life scenarios such as surveillance videos, each image set often contains much redundancy in terms of features and samples. This paper introduces a joint learning method for image-set classification that simultaneously learns compact binary codes and removes redundant samples. The joint objective function of our model mainly includes two parts. The first part seeks a hashing function to generate binary codes that have larger inter-class and smaller intra-class distances. The second one reduces redundant samples with discrete constraints in a low-rank way. A kernel method based on anchor points is further used to reduce sample variations. The proposed discrete objective function is simplified to a series of sub-problems that admit an analytical solution, resulting in a high-quality discrete solution with a low computational cost. Experiments on three commonly used image-set datasets show that the proposed method for the tasks of face recognition from image sets is efficient and effective.

Authors

• Man Zhang Harbin Institute of Technology
• Ran He NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Dong Cao
• Zhenan Sun School of Artificial Intelligence, University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences
• Tieniu Tan Nanjing University NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences University of Chinese Academy of Sciences

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