? 有判别性的直方图交叉度量学习及其应用
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (3) :507-519    DOI: 10.1007/s11390-017-1740-0
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | Next Articles >>
有判别性的直方图交叉度量学习及其应用
Peng-Yi Hao 1, Member, CCF, IEEE, Yang Xia 1, Xiao-Xin Li1, Sei-ichiro Kamata 2, Member, IEEE, Sheng-Yong Chen 1, Senior Member, CCF, IEEE
1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;
2. Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan
Discriminative Histogram Intersection Metric Learning and Its Applications
Peng-Yi Hao 1, Member, CCF, IEEE, Yang Xia 1, Xiao-Xin Li1, Sei-ichiro Kamata 2, Member, IEEE, Sheng-Yong Chen 1, Senior Member, CCF, IEEE
1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China;
2. Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan

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摘要 本文提出了一个新颖的度量学习方法,即:有辨别性的直方图交叉度量学习。明确地说,本文的方法从二值信息例如一样/不一样或者相似/不相似中学习一种度量机制,并且结合了分类错误率来提高分类机制中的可判别性。跟传统方法相比,提出的方法有以下几个优势:1)将直方图交叉策略引入度量学习,可以有效地解决被广泛使用的直方图特征,如SIFT,LBP等;2)本文方法可以同时学习有判别性的距离度量并训练分类器;3)本文方法中的目标函数对训练所用特征和标记中的噪声很鲁棒。本文方法在人脸验证,人脸序列认证,人脸序列聚类以及图像分类四个应用中进行了评价,通过在公开数据库上的验证,以及与其他方法的比较,结果表明本文方法具有更好的鲁棒性和可判别性。
关键词度量学习   对匹配   图像分类   人脸验证     
Abstract: In this paper, a novel method called discriminative histogram intersection metric learning (DHIML) is proposed for pair matching and classification. Specifically, we introduce a discrimination term for learning a metric from binary information such as same/not-same or similar/dissimilar, and then combine it with the classification error for the discrimination in classifier construction. Compared with conventional approaches, the proposed method has several advantages. 1) The histogram intersection strategy is adopted into metric learning to deal with the widely used histogram features effectively. 2) By introducing discriminative term and classification error term into metric learning, a more discriminative distance metric and a classifier can be learned together. 3) The objective function is robust to outliers and noises for both features and labels in the training. The performance of the proposed method is tested on four applications: face verification, face-track identification, face-track clustering, and image classification. Evaluations on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark, a dataset with more than 7000 face-tracks, and Caltech-101 dataset validate the robustness and discriminability of the proposed metric learning, compared with the recent state-of-the-art approaches.
Keywordsmetric learning   pair matching   image classification   face verification     
Received 2016-12-25;
本文基金:

This work was supported by the Natural Science Foundation of Zhejiang Province of China under Grant Nos. LQ15F020008 and LY15F020028, the National Natural Science Foundation of China under Grant Nos. 61325019, 61402411, 61502424, and U1509207, and Japan Society for the Promotion of Science (JSPS KAKENHI) under Grant No. 15K00248.

About author: Peng-Yi Hao received her Ph.D. degree in computer science from Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, in 2013. She is currently a lecturer of Zhejiang University of Technology, Hangzhou. Her current research interests include computer vision and image analysis. She is a member of CCF and IEEE.
引用本文:   
Peng-Yi Hao, Yang Xia, Xiao-Xin Li, Sei-ichiro Kamata, Sheng-Yong Chen.有判别性的直方图交叉度量学习及其应用[J]  Journal of Computer Science and Technology , 2017,V32(3): 507-519
Peng-Yi Hao, Yang Xia, Xiao-Xin Li, Sei-ichiro Kamata, Sheng-Yong Chen.Discriminative Histogram Intersection Metric Learning and Its Applications[J]  Journal of Computer Science and Technology, 2017,V32(3): 507-519
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