A deep feature‐based semi‐supervised collaborative training for vehicle recognition in smart cities

2021 ◽  
Author(s):  
Yichuan Zhang ◽  
Yadi Liu ◽  
Guangming Yang ◽  
Jie Song
Keyword(s):  
Smart Cities ◽  
Vehicle Recognition ◽  
Deep Feature ◽  
Feature Based ◽  
Collaborative Training

scholarly journals Deep Feature Based on Convolutional Auto-Encoder for Compact Semantic Hashing

2019 ◽  
Vol 1229 ◽  
pp. 012032
Author(s):  
Jun Wang ◽  
Jian Zhou ◽  
Liangding Li ◽  
Jiapeng Chi ◽  
Feiling Yang ◽  
...  
Keyword(s):  
Deep Feature ◽  
Feature Based

Research on Area-Feature-Based Filter Algorithm for Vehicle Recognition System

2010 ◽  
Vol 20-23 ◽  
pp. 998-1003 ◽  
Author(s):  
Wei Hua Wang ◽  
Wei Qing Wang
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Vehicle Recognition ◽  
Central Processing ◽  
Noise Filter ◽  
Contour Feature ◽  
Feature Based ◽  
Traffic Video ◽  
Real Vehicle

Feature extraction is a central processing in the automatic target recognition system, and noise filter is an important step in the feature extraction. A lot of research in noise filter has been proposed and used in image processing. In this paper, a noise filter algorithm based on area feature for vehicle recognition system is presented. Discussing the feature of vehicle for the recognition system, and the problem of the vehicle contour feature obtained from the traffic video. Analyzing the principle of the solve approach of the area-feature-based noise filter for vehicle recognition system. Implementing the program of the algorithm proposed in the following. Experiments have been conducted by many real vehicle images obtained from a real-time video produced by a monitor. The result shows that the new proposed method can remove the noise of the image signal for the vehicle recognition efficiently.

Deep Feature-Based Classifiers for Fruit Fly Identification (Diptera: Tephritidae)

2018 ◽  
Author(s):  
Matheus Macedo Leonardo ◽  
Tiago J. Carvalho ◽  
Edmar Rezende ◽  
Roberto Zucchi ◽  
Fabio Augusto Faria
Keyword(s):  
Fruit Fly ◽  
Deep Feature ◽  
Feature Based

Two-Stream Deep Feature-Based Froth Flotation Monitoring Using Visual Attention Clues

2021 ◽  
Vol 70 ◽  
pp. 1-14
Author(s):  
Mingxi Ai ◽  
Yongfang Xie ◽  
Zhaohui Tang ◽  
Jin Zhang ◽  
Weihua Gui
Keyword(s):  
Visual Attention ◽  
Froth Flotation ◽  
Deep Feature ◽  
Feature Based

scholarly journals Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

2019 ◽  
Vol 11 (23) ◽  
pp. 2870
Author(s):  
Chu He ◽  
Qingyi Zhang ◽  
Tao Qu ◽  
Dingwen Wang ◽  
Mingsheng Liao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Feature Fusion ◽  
Texture Features ◽  
Texture Image ◽  
Binary Coding ◽  
Deep Feature ◽  
Feature Based ◽  
Texture Image Classification

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms.

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