scholarly journals Impostor Resilient Multimodal Metric Learning for Person Reidentification

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Muhamamd Adnan Syed ◽  
Zhenjun Han ◽  
Zhaoju Li ◽  
Jianbin Jiao
Keyword(s):  
Real World ◽  
Metric Learning ◽  
Image Space ◽  
Distance Metrics ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Benchmark Datasets ◽  
Query Sample

In person reidentification distance metric learning suffers a great challenge from impostor persons. Mostly, distance metrics are learned by maximizing the similarity between positive pair against impostors that lie on different transform modals. In addition, these impostors are obtained from Gallery view for query sample only, while the Gallery sample is totally ignored. In real world, a given pair of query and Gallery experience different changes in pose, viewpoint, and lighting. Thus, impostors only from Gallery view can not optimally maximize their similarity. Therefore, to resolve these issues we have proposed an impostor resilient multimodal metric (IRM3). IRM3 is learned for each modal transform in the image space and uses impostors from both Probe and Gallery views to effectively restrict large number of impostors. Learned IRM3 is then evaluated on three benchmark datasets, VIPeR, CUHK01, and CUHK03, and shows significant improvement in performance compared to many previous approaches.

scholarly journals Distance metric learning for graph structured data

2021 ◽  
Author(s):  
Tomoki Yoshida ◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama
Keyword(s):  
Metric Learning ◽  
Structured Data ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

Out-of-Distribution Detection Based on Distance Metric Learning

2020 ◽  
Author(s):  
Donghun Yang ◽  
Iksoo Shin ◽  
Mai Ngoc Kien ◽  
Hoyong Kim ◽  
Chanhee Yu ◽  
...  
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric
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