[2016-09-15]: We are organizing a special issue on Distance Metric Learning for Pattern Recognition at the Pattern Recognition Journal. Please visit here for more details.

Tutorial Abstract

Over the past decade, distance metric learning has been developed as one of the basic techniques in machine learning and successfully applied to a wide range of computer vision tasks showing state-of-the-art performance. In this tutorial, we will overview the trend of distance metric learning techniques and discuss how they are employed to boost the performance of various computer vision tasks. First, we briefly introduce the basic concept of distance metric learning, and show the key advantages and disadvantages of existing distance metric learning methods in different computer recognition tasks. Second, we introduce some of our newly proposed distance metric learning methods from two aspects: sample-based metric learning and set-based metric learning, which are developed for different application-specific computer vision tasks, respectively. Lastly, we will discuss some open problems in distance metric learning to show how to further develop more advanced metric learning algorithms for computer vision in the future.

Target Audience

This tutorial is addressed to faculty, researchers, PhD students, MS students, and engineers who are working on related topics, e.g., metric learning, subspace learning, deep learning, manifold learning, and their applications to different visual recognition tasks. The tutorial is based on matrix theory and requires minimum knowledge of convex optimization and some basic knowledge in graduate-level computer vision and linear algebra.

Tutorial Outline

  • 14:00: Introduction
  • 14:10: Sample-Based Metric Learning and Its Applications [PDF]
    • Cost-Sensitive Metric Learning
    • Locality Repulsed Metric Learning
    • Collaborative Metric Learning
    • Multi-View Metric Learning
    • Deep Metric Learning
  • 15:40pm: Set-Based Metric Learning and Its Applications [PDF]
      
    • Covariance-based Metric Learning
    • Manifold-based Metric learning
    • Dictionary-Based Metric Learning
    • Multi-Instance Metric Learning
    • Point-to-set Metric Learning
  • 16:50pm: Open Questions and Discussions [PDF]

Relevant References

  1. Jiwen Lu, Venice Erin Liong, Xiuzhuang Zhou, and Jie Zhou, Learning Compact Binary Face Descriptor for Face Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 10, pp. 2041-2056, 2015.
  2. Jiwen Lu, Xiuzhuang Zhou, Yap-Peng Tan, Yuanyuan Shang, and Jie Zhou, Neighborhood Repulsed Metric Learning for Kinship Verification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 2, pp. 331-345, 2014.
  3. Jiwen Lu, Yap-Peng Tan, and Gang Wang, Discriminative Multimanifold Analysis for Face Recognition from A Single Training Sample Per Person, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 39-51, 2013.
  4. Ruiping Wang, Shiguang Shan, Xilin Chen, Jie Chen, and Wen Gao, Maximal Linear Embedding for Dimensionality Reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 9, pp. 1776-1792, 2011.
  5. Junlin Hu, Jiwen Lu, and Yap-Peng Tan, Deep Metric Learning for Visual Tracking, IEEE Transactions on Circuits and Systems for Video Technology, 2016, accepted.
  6. Jiwen Lu, Venice Erin Liong, and Jie Zhou, Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation, IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5356-5368, 2015.
  7. Zhiwu Huang, Shiguang Shan, Ruiping Wang, Haihong Zhang, Shihong Lao, Alifu Kuerban, Xilin Chen, A Benchmark and Comparative Study of Video-based Face Recognition on COX Face Database, IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5967-5981, Dec. 2015.
  8. Ruiping Wang, Shiguang Shan, Xilin Chen, Qionghai Dai, Wen Gao, Manifold-Manifold Distance and Its Application to Face Recognition with Image Sets, IEEE Transactions on Image Processing, vol. 21, no. 10, pp. 4466-4479, Oct. 2012.
  9. Kevin Lin, Jiwen Lu, Chu-Song Chen, and Jie Zhou, Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks, IEEE CVPR, 2016.
  10. Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen, Deep Supervised Hashing for Fast Image Retrieval, IEEE CVPR, 2016.
  11. Jiwen Lu, Venice Erin Liong, and Jie Zhou, Simultaneous Local Binary Feature Learning and Encoding for Face Recognition, IEEE ICCV, 2015.
  12. Anran Wang, Jianfei Cai, Jiwen Lu, and Tat Jen Cham, MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition, IEEE ICCV, 2015.
  13. Yan Li, Ruiping Wang, Haomiao Liu, Huajie Jiang, Shiguang Shan, Xilin Chen, Two Birds, One Stone: Jointly Learning Binary Code for Large-scale Face Image Retrieval and Attributes Prediction, IEEE ICCV, 2015.
  14. Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xianqiu Li, Xilin Chen, Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification, ICML, 2015.
  15. Jiwen Lu, Gang Wang, Weihong Deng, Pierre Moulin, and Jie Zhou, Multi-Manifold Deep Metric Learning for Image Set Classification, IEEE CVPR, Boston, Jun., 2015.
  16. Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, and Jie Zhou, Deep Hashing for Compact Binary Codes Learning, IEEE CVPR, Boston, Jun., 2015.
  17. Junlin Hu, Jiwen Lu, and Yap-Peng Tan, Deep Transfer Metric Learning, IEEE CVPR, Boston, Jun., 2015.
  18. Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen, Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition, IEEE CVPR, Boston, Jun., 2015.
  19. Yan Li, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen, Face Video Retrieval with Image Query via Hashing across Euclidean Space and Riemannian Manifold, IEEE CVPR, Boston, Jun., 2015.
  20. Wen Wang, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen, Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition with Image Sets, IEEE CVPR, Boston, Jun., 2015.
  21. Jiwen Lu, Gang Wang, Weihong Deng, and Pierre Moulin, Simultaneous Feature and Dictionary Learning for Image Sets Based Face Recognition, ECCV, pp. 265-280, 2014.
  22. Junlin Hu, Jiwen Lu, and Yap-Peng Tan, Discriminative Deep Metric Learning for Face Verification in the Wild, IEEE CVPR, pp. 1875-1882, 2014.
  23. Zhiwu Huang, Ruiping Wang, Shiguang Shan, Xilin Chen, Learning Euclidian-to-Riemannian metric for point-to-set classification, IEEE CVPR, pp. 1677-1684, 2014.
  24. Mengyi Liu, Shiguang Shan, Ruiping Wang, Xilin Chen, Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition, IEEE CVPR, pp. 1749-1756, 2014.
  25. Jiwen Lu, Gang Wang, and Pierre Moulin, Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-Kernel Metric Learning, IEEE ICCV, pp. 329-336, 2013.
  26. Renliang Weng, Jiwen Lu, Junlin Hu, Gao Yang, and Yap-Peng Tan, Robust Feature Set Matching for Partial Face Recognition, IEEE ICCV, pp. 601-608, 2013.
  27. Ruiping Wang, Huimin Guo, Larry S. Davis, and Qionghai Dai, Covariance Discriminative Learning: A Natural and Efficient Approach to Image Set Classification, IEEE CVPR, pp.2496-2503, 2012.
  28. Jiwen Lu and Yap-Peng Tan, Cost-Sensitive Subspace Learning for Face Recognition, IEEE CVPR, pp. 2661-2666, 2010.
  29. Ruiping Wang and Xilin Chen, Manifold Discriminant Analysis, IEEE CVPR, pp. 429- 436, 2009.
  30. Ruiping Wang, Shiguang Shan, Xilin Chen, and Wen Gao, Manifold-Manifold Distance With Application to Face Recognition Based on Image Sets, IEEE CVPR, pp. 1-8, 2008.