A Semi-Supervised High-Level Feature Selection Framework for Road Centerline Extraction

2020 ◽  
Vol 17 (5) ◽  
pp. 894-898
Author(s):  
Ruyi Liu ◽  
Qiguang Miao ◽  
Yi Zhang ◽  
Maoguo Gong ◽  
Pengfei Xu
Keyword(s):  
Feature Selection ◽  
Centerline Extraction ◽  
Selection Framework ◽  
High Level ◽  
High Level Feature

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.

Deep AI Enabled Ubiquitous Wireless Sensing

2021 ◽  
Vol 54 (2) ◽  
pp. 1-35
Author(s):  
Chenning Li ◽  
Zhichao Cao ◽  
Yunhao Liu
Keyword(s):  
Great Success ◽  
Wireless Sensing ◽  
Ubiquitous Sensing ◽  
Sensing Systems ◽  
The Status ◽  
Sensing Model ◽  
High Level ◽  
Traditional Approaches ◽  
The Internet Of Things ◽  
High Level Feature

With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.

scholarly journals An ensemble feature selection framework for early detection of Parkinson's disease based on feature correlation analysis

2021 ◽  
Author(s):  
Sarfaraz Masood ◽  
Khwaja Wisal ◽  
Om Pal ◽  
Chanchal Kumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Large Population ◽  
Feature Selection Method ◽  
Sampling Strategy ◽  
Chi Square ◽  
Accurate Identification ◽  
Initial Symptoms ◽  
Selection Framework

Parkinson’s disease (PD) is a highly common neurological disease affecting a large population worldwide. Several studies revealed that the degradation of voice is one of its initial symptoms, which is also known as dysarthria. In this work, we attempt to explore and harness the correlation between various features in the voice samples observed in PD subjects. To do so, a novel two-level ensemble-based feature selection method has been proposed, whose results were combined with an MLP based classifier using K-fold cross-validation as the re-sampling strategy. Three separate benchmark datasets of voice samples were used for the experimentation work. Results strongly suggest that the proposed feature selection framework helps in identifying an optimal set of features which further helps in highly accurate identification of PD patients using a Multi-Layer Perceptron from their voice samples. The proposed model achieves an overall accuracy of 98.3%, 95.1% and 100% on the three selected datasets respectively. These results are significantly better than those achieved by a non-feature selection based option, and even the recently proposed chi-square based feature selection option.

Sign in / Sign up

Export Citation Format

Share Document