Simultaneous Feature and Sample Reduction for Image-Set Classification

Authors

  • Man Zhang Institue of Automation, Chinese Academy of Sciences
  • Ran He Institue of Automation, Chinese Academy of Sciences
  • Dong Cao Institue of Automation, Chinese Academy of Sciences
  • Zhenan Sun Institue of Automation, Chinese Academy of Sciences
  • Tieniu Tan Institue of Automation, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v30i1.10156

Abstract

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.

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Published

2016-02-21

How to Cite

Zhang, M., He, R., Cao, D., Sun, Z., & Tan, T. (2016). Simultaneous Feature and Sample Reduction for Image-Set Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10156

Issue

Section

Technical Papers: Machine Learning Applications