An efficient approach for facial action unit intensity detection using distance metric learning based on cosine similarity

2018 ◽  
Vol 12 (6) ◽  
pp. 1141-1148 ◽  
Author(s):  
Neeru Rathee ◽  
Dinesh Ganotra
Keyword(s):  
Metric Learning ◽  
Cosine Similarity ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Action Unit ◽  
Facial Action ◽  
Efficient Approach

Facial Action Unit Intensity Detection by Extracting Complimentary Information using Distance Metric Learning

2018 ◽  
Vol 66 (2) ◽  
pp. 214-223 ◽  
Author(s):  
Neeru Rathee ◽  
Dinesh Ganotra ◽  
Ajay Rathee
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Action Unit ◽  
Facial Action

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

Eigenvector-Based Distance Metric Learning for Image Classification and Retrieval

2019 ◽  
Vol 15 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Zhangcheng Wang ◽  
Ya Li ◽  
Richang Hong ◽  
Xinmei Tian
Keyword(s):  
Image Classification ◽  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric

Semi-supervised LDA Based Method for Similarity Distance Metric Learning

2021 ◽  
Author(s):  
Ren Deng ◽  
Yaxuan Chen ◽  
Ruilin Han ◽  
Han Xiao ◽  
Xijie Li
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Similarity Distance

scholarly journals Application of Distance Metric Learning to Automated Malware Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Martin Jurecek ◽  
Robert Lorencz
Keyword(s):  
Metric Learning ◽  
Malware Detection ◽  
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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