Improving Features Extraction for Supervised Invoice Classification

2010 ◽  
Author(s):  
A. Bartoli ◽  
G. Davanzo ◽  
E. Medvet ◽  
E. Sorio
Keyword(s):  
Features Extraction

An Investigation on Gesture Analysis and Geometric Features Extraction

2015 ◽  
Vol 03 (01) ◽  
pp. 387-392
Author(s):  
NOOR ADNAN IBRAHEEM ◽  
MOKHTAR MOHAMMED HASAN ◽  
RAFIQULZAMAN KHAN
Keyword(s):  
Features Extraction ◽  
Geometric Features ◽  
Gesture Analysis

scholarly journals Signature Texture Features Extraction Using GLCM Approach in Android Studio

2021 ◽  
Vol 1804 (1) ◽  
pp. 012043
Author(s):  
Hussein Awad Dwaich ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Texture Features ◽  
Features Extraction

scholarly journals Person Re-Identification Based on Attention Mechanism and Context Information Fusion

2021 ◽  
Vol 13 (3) ◽  
pp. 72
Author(s):  
Shengbo Chen ◽  
Hongchang Zhang ◽  
Zhou Lei
Keyword(s):  
Information Fusion ◽  
Irrelevant Information ◽  
Features Extraction ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Context Information ◽  
Feature Map ◽  
High Level ◽  
Surveillance Analysis ◽  
High Level Feature

Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

scholarly journals Classification of PD Faults Using Features Extraction and K-Means Clustering Techniques

2020 ◽  
Author(s):  
Haresh Kumar ◽  
Muhammad Shafiq ◽  
Ghulam Amjad Hussain ◽  
Lauri Kumpulainen ◽  
Kimmo Kauhaniemi
Keyword(s):  
Features Extraction ◽  
Clustering Techniques
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