H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 4,536 217 World Ranking 8794 National Ranking 832

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Xiao-Yuan Jing mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Computer vision. The study incorporates disciplines such as Software and Rank in addition to Artificial intelligence. His research ties Image and Pattern recognition together.

His Machine learning research includes elements of Test data, Data mining and Variable. Xiao-Yuan Jing works mostly in the field of Discriminative model, limiting it down to topics relating to Discriminant and, in certain cases, Identification. His Linear discriminant analysis research includes themes of Normalization and Signature recognition.

His most cited work include:

  • A face and palmprint recognition approach based on discriminant DCT feature extraction (217 citations)
  • Super-resolution Person re-identification with semi-coupled low-rank discriminant dictionary learning (133 citations)
  • Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition (132 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Pattern recognition, Facial recognition system, Machine learning and Feature extraction. His research investigates the connection between Artificial intelligence and topics such as Computer vision that intersect with problems in Convolution. His work deals with themes such as Subspace topology and Image, which intersect with Pattern recognition.

His Facial recognition system study incorporates themes from Image processing, Pixel and Kernel. His studies deal with areas such as Contextual image classification, Representation, Data mining and Metric as well as Machine learning. His Feature extraction study incorporates themes from Manifold alignment, Nonlinear dimensionality reduction, Feature, Projection and Pattern recognition.

He most often published in these fields:

  • Artificial intelligence (98.25%)
  • Pattern recognition (67.37%)
  • Facial recognition system (26.67%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (98.25%)
  • Pattern recognition (67.37%)
  • Computer vision (24.21%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Feature are his primary areas of study. His Artificial intelligence research includes themes of Machine learning and Metric. His Pattern recognition research focuses on Feature vector in particular.

His Computer vision research integrates issues from Attention network and Convolution. The Discriminative model study combines topics in areas such as Feature, Re identification, Feature learning, Benchmark and Convolutional neural network. His study looks at the intersection of Facial recognition system and topics like Set with Identification and Empirical research.

Between 2019 and 2021, his most popular works were:

  • Multiset Feature Learning for Highly Imbalanced Data Classification (13 citations)
  • Modality-specific and shared generative adversarial network for cross-modal retrieval (12 citations)
  • Dynamic attention network for semantic segmentation (12 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

His primary areas of investigation include Artificial intelligence, Machine learning, Discriminative model, Computer vision and Metric. His study connects Pattern recognition and Artificial intelligence. His study in Machine learning is interdisciplinary in nature, drawing from both Multiset, Discriminant and Partition.

His Discriminative model research is multidisciplinary, relying on both Multispectral image, Facial recognition system, Face, Benchmark and Convolutional neural network. Xiao-Yuan Jing interconnects Attention network, Pyramid and Pascal in the investigation of issues within Computer vision. His biological study spans a wide range of topics, including Software bug, Interpretability, Empirical research and Field.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A face and palmprint recognition approach based on discriminant DCT feature extraction

Xiao-Yuan Jing;D. Zhang.
systems man and cybernetics (2004)

328 Citations

Super-resolution Person re-identification with semi-coupled low-rank discriminant dictionary learning

Xiao-Yuan Jing;Xiaoke Zhu;Fei Wu;Xinge You.
computer vision and pattern recognition (2015)

210 Citations

Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition

Xiao-Yuan Jing;Yong-Fang Yao;David Zhang;Jing-Yu Yang.
Pattern Recognition (2007)

193 Citations

Dictionary learning based software defect prediction

Xiao-Yuan Jing;Shi Ying;Zhi-Wu Zhang;Shan-Shan Wu.
international conference on software engineering (2014)

181 Citations

Letters: Face and palmprint feature level fusion for single sample biometrics recognition

Yong-Fang Yao;Xiao-Yuan Jing;Hau-San Wong.
Neurocomputing (2007)

165 Citations

Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning

Xiaoyuan Jing;Fei Wu;Xiwei Dong;Fumin Qi.
foundations of software engineering (2015)

150 Citations

Rapid and brief communication: Face recognition based on 2D Fisherface approach

Xiao-Yuan Jing;Hau-San Wong;David Zhang.
Pattern Recognition (2006)

121 Citations

Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics

Xiaoke Zhu;Xiao-Yuan Jing;Xinge You;Xinyu Zhang.
IEEE Transactions on Image Processing (2018)

118 Citations

An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems

Xiao-Yuan Jing;Fei Wu;Xiwei Dong;Baowen Xu.
IEEE Transactions on Software Engineering (2017)

105 Citations

Multi-view low-rank dictionary learning for image classification

Fei Wu;Xiao-Yuan Jing;Xinge You;Dong Yue.
Pattern Recognition (2016)

104 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Xiao-Yuan Jing

David Zhang

David Zhang

Chinese University of Hong Kong, Shenzhen

Publications: 28

Wei-Shi Zheng

Wei-Shi Zheng

Sun Yat-sen University

Publications: 16

Yong Xu

Yong Xu

Harbin Institute of Technology

Publications: 15

Ahmed Bouridane

Ahmed Bouridane

Northumbria University

Publications: 14

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 12

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 9

Yun Fu

Yun Fu

Northeastern University

Publications: 9

Tieniu Tan

Tieniu Tan

Chinese Academy of Sciences

Publications: 8

Duoqian Miao

Duoqian Miao

Tongji University

Publications: 8

Hamid Krim

Hamid Krim

North Carolina State University

Publications: 8

Xiaojun Wu

Xiaojun Wu

University of Science and Technology of China

Publications: 8

Dapeng Tao

Dapeng Tao

Yunnan University

Publications: 8

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 8

Lei Zhang

Lei Zhang

Hong Kong Polytechnic University

Publications: 7

Xiapu Luo

Xiapu Luo

Hong Kong Polytechnic University

Publications: 7

Junjun Jiang

Junjun Jiang

Harbin Institute of Technology

Publications: 7

Something went wrong. Please try again later.