Emerging Applications of Image Retrieval and Recognition Technology in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 7635

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College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China
Interests: computer vision; machine learning; intelligent optimization control
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Guest Editor
School of Electronic Information, Sichuan University, Chengdu 610064, China
Interests: image processing; pattern recognition

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Guest Editor
College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China
Interests: computer vision; machine learning; intelligent optimization control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: remote sensing; deep learning; artificial intelligence; image processing; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emerging applications of image retrieval and recognition technology (IRRT) in smart cities include smart healthcare, smart energy, smart transportation, smart environment, smart safety and smart education. In smart healthcare, IRRT could help to identify a given case based on similar past cases found in the image archive. In smart transportation, IRRT could help to recognize traffic patterns by investigating image sequences acquired in real time. In a smart environment, IRRT could help to predict weather information and hazardous conditions. In smart safety, IRRT could help to predict natural disasters. In smart education, IRRT could help to make new discoveries using high-quality digital images taken from different research fields.

However, there are still challenges that need to be addressed to accelerate the development of these emerging applications. Firstly, the current techniques for feature extraction cannot adequately represent an image. More effective and robust feature-extraction methods are urgently required. Secondly, the image datasets are not large enough to meet the practical needs of the emerging applications in smart cities. Thirdly, current image retrieval and recognition technology cannot satisfy real-time requirements when the image datasets are large enough to meet the practical needs.

This Special Issue will focus on (but will not be limited to) the following topics:

  • Image datasets for the emerging applications in smart cities;
  • Effective features for image retrieval and recognition;
  • Content-based image retrieval methods;
  • Object recognition methods;
  • Image-sensing devices and methods;
  • Challenges of IRRT in smart cities;
  • More emerging applications of IRRT in smart cities;
  • Image-processing techniques that have the potential to be used in IRRT.

Technical Committee Member:
Dr. Lina Liu  Shandong University of Technology

Prof. Dr. Zhenzhou Wang
Prof. Dr. Xiaomin Yang
Dr. Mingliang Gao
Dr. Gwanggil Jeon
Guest Editor

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Keywords

  • content-based image retrieval
  • feature extraction
  • object recognition
  • image datasets
  • smart city

Published Papers (2 papers)

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Research

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19 pages, 7418 KiB  
Article
Vehicle Logo Detection Method Based on Improved YOLOv4
by Xiaoli Jiang, Kai Sun, Liqun Ma, Zhijian Qu and Chongguang Ren
Electronics 2022, 11(20), 3400; https://doi.org/10.3390/electronics11203400 - 20 Oct 2022
Cited by 9 | Viewed by 1868
Abstract
A vehicle logo occupies a small proportion of a car and has different shapes. These characteristics bring difficulties to machine-vision-based vehicle logo detection. To improve the accuracy of vehicle logo detection in complex backgrounds, an improved YOLOv4 model was presented. Firstly, the CSPDenseNet [...] Read more.
A vehicle logo occupies a small proportion of a car and has different shapes. These characteristics bring difficulties to machine-vision-based vehicle logo detection. To improve the accuracy of vehicle logo detection in complex backgrounds, an improved YOLOv4 model was presented. Firstly, the CSPDenseNet was introduced to improve the backbone feature extraction network, and a shallow output layer was added to replenish the shallow information of small target. Then, the deformable convolution residual block was employed to reconstruct the neck structure to capture the various and irregular shape features. Finally, a new detection head based on a convolutional transformer block was proposed to reduce the influence of complex backgrounds on vehicle logo detection. Experimental results showed that the average accuracy of all categories in the VLD-45 dataset was 62.94%, which was 5.72% higher than the original model. It indicated that the improved model could perform well in vehicle logo detection. Full article
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Review

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15 pages, 18902 KiB  
Review
Crime Scene Shoeprint Image Retrieval: A Review
by Yanjun Wu, Xianling Dong, Guochao Shi, Xiaolei Zhang and Congzhe Chen
Electronics 2022, 11(16), 2487; https://doi.org/10.3390/electronics11162487 - 10 Aug 2022
Cited by 3 | Viewed by 4514
Abstract
Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to [...] Read more.
Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to the query image. In this way, a shoeprint can not only be used as an exhibit for bringing criminal charges but also to provide a clue to a case. The goal of this work is to present an overview of the existing works conducted in shoeprint image retrieval. We detail the different phases of the shoeprint retrieval task and present a summary of the state-of-the-art methods. We analyzed the difficulties and problems in this field and discussed future work directions. This review may help neophytes become involved in research easily and quickly. Full article
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