Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations

With the development of multimedia time, one sample can always be described from multiple views which contain compatible and complementary information. Most algorithms cannot take information from multiple views into considerations and fail to achieve desirable performance in most situations. For many applications, such as image retrieval, face recognition, etc., an appropriate distance metric can better reflect the similarities between various samples. Therefore, how to construct a good distance metric learning methods which can deal with multiview data has been an important topic during the last decade. In this paper, we proposed a novel algorithm named Self-weighted Multiview Metric Learning (SM2L) which can finish this task by maximizing the cross correlations between different views. Furthermore, because multiple views have different contributions to the learning procedure of SM2L, we adopt a self-weighted learning framework to assign multiple views with different weights. Various experiments on benchmark datasets can verify the performance of our proposed method.

[1]  Jing Zhang,et al.  Semantic Discriminative Metric Learning for Image Similarity Measurement , 2016, IEEE Transactions on Multimedia.

[2]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[3]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[5]  Lin Feng,et al.  Learning a Distance Metric by Balancing KL-Divergence for Imbalanced Datasets , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[7]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[8]  Yongdong Zhang,et al.  Multiview Spectral Embedding , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Meng Wang,et al.  3-D PersonVLAD: Learning Deep Global Representations for Video-Based Person Reidentification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Lin Feng,et al.  Multi-view metric learning based on KL-divergence for similarity measurement , 2017, Neurocomputing.

[11]  Wen Gao,et al.  Multiview Metric Learning with Global Consistency and Local Smoothness , 2012, TIST.

[12]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

[13]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[14]  Lin Wu,et al.  Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Fei Wang,et al.  Semisupervised Metric Learning by Maximizing Constraint Margin , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[17]  Geoffrey E. Hinton,et al.  Neighbourhood Components Analysis , 2004, NIPS.

[18]  Lin Wu,et al.  Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval , 2017, IEEE Transactions on Image Processing.

[19]  Lin Wu,et al.  Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering , 2016, IJCAI.

[20]  Lin Wu,et al.  Deep adaptive feature embedding with local sample distributions for person re-identification , 2017, Pattern Recognit..

[21]  Rui Hu,et al.  A performance evaluation of gradient field HOG descriptor for sketch based image retrieval , 2013, Comput. Vis. Image Underst..

[22]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Ling Shao,et al.  Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval , 2018, IEEE Transactions on Image Processing.

[24]  Lin Wu,et al.  Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus , 2015, IEEE Transactions on Image Processing.

[25]  Lin Wu,et al.  Effective Multi-Query Expansions: Robust Landmark Retrieval , 2015, ACM Multimedia.

[26]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[27]  David W. Jacobs,et al.  Generalized Multiview Analysis: A discriminative latent space , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.