信 息 科 学 与 工 程 学 院

College of Information Science and Engineering

厚德笃志,勤思敏行

基本信息

basic information

  • 所在院系(中心): 计算机科学与技术系
  • 学历: 博士
  • 政治面貌: 中共党员
  • 邮件地址: gqzhong@ouc.edu.cn
  • 办公电话: 0532-66781719
  • 办公室: 山东省青岛市崂山区松岭路238号中国海洋大学信息科学与工程学院

仲国强

Zhong GuoQiang

课程介绍

course introduction

图论(本科), 深度学习基础(硕士), 形式化方法(硕士), 机器学习(博士)

教育及工作经历

education and work experience

2014年3月至今: 由中国海洋大学 "青年英才工程" (第三层次) 项目引进, 副教授, 硕士生导师
2011.10-2013.7: 博士后, 合作导师: Mohamed Cheriet教授, 魁北克大学高等技术学院, 蒙特利尔, 加拿大
2009.9-2010.1: 访问学生, 导师: Dit-Yan Yeung教授, 香港科技大学, 香港, 中国
2007.9-2011.7: 工学博士, 导师: 刘成林研究员, 中国科学院自动化研究所, 北京, 中国
2004.9-2007.7: 理学硕士, 导师: 孟大志教授, 北京工业大学, 北京, 中国
2000.9-2004.7: 理学学士, 河北师范大学, 石家庄, 中国

2015.1至今: ACM会员, IEEE会员, IAPR会员, 中国计算机学会会员, 中国人工智能学会会员, 中国自动化学会会员, 中国图象图形学学会会员
2014.11至今: 中国人工智能学会模式识别专业委员会委员, 中国自动化学会模式识别与机器智能专委会委员, 中国图象图形学学会会员文档图像分析与识别专业委员会委员

研究方向

research direction

人工智能, 机器学习, 模式识别, 计算机视觉, 大数据挖掘, 机器人, 及相关技术的应用.

研究项目

research project

1. 面向自然环境中文字检测与识别的深度网络精简技术研究, CCF-腾讯犀牛鸟创意基金, 项目主持人, 2018.1-2018.12.
2. 服务于视障人群的智能穿戴设备研发, 青岛市产业培育计划科技惠民专项, 项目主持人, 2018.1-2019.12.
3. 监督的深度学习算法及其在海洋环境数据分析中的应用, 国家自然科学基金青年基金项目(NSFC), 项目主持人, 2015.1 -- 2017.12.
4. 深度学习和大规模数据挖掘算法及其应用研究, 中央高校基本科研业务费专项项目("青年英才工程"启动经费), 项目主持人, 2014.3 -- 2019.2.
5. 深度小波网络模型及其应用研究, 模式识别国家重点实验室开放课题基金项目, 项目主持人, 2015.1 -- 2016.12. (已结题)
6. 海洋大数据分析预报技术研发, 国家重点研发计划项目, 参与.
7. 海洋科学研究中的范式转型与对策研究, 中国海洋发展研究会项目, 参与.
8. 科技期刊微信平台消息推送的分析与策略, 中国科学技术期刊编辑学会基金项目, 参与.
9. 海洋锋精细化识别与时空演化的多角度可视化探索, 国家自然科学基金青年基金项目(NSFC), 参与.

学术成果

academic achievements

1. 机器学习导论. 机械工业出版社, 2016. (译著, 作者: 王勇, 仲国强, 孙鑫)
2. 2014-2015, Outstanding Reviewer Award for the Pattern Recognition journal, Elsevier.
3. 2015, 中国大学生“华为杯”智能设计竞赛三等奖, 两项.
4. 2017, 中国大学生“华为杯”智能设计竞赛二等奖, 两项.
5. 2017, 中国大学生“华为杯”智能设计竞赛华为专项奖, 两项.
6. 2017, 中国海洋大学本科生毕业论文(设计)优秀指导教师.
7. 会议(协同)主席: ICGIP2017 publication chair; ISSPA2012 poster session chair; IDAS2014 chair; IDAS2015 chair; IJCNN2016 special session chair on Deep Learning for Brain-Like Computing and Pattern Recognition; CAAI-PR Expert Forum program chair (2016); CCF-CV Series Lectures program chair (2016)等.
8. 期刊会议审稿人: ACM TKDD; IEEE TNNLS; IEEE TCSVT; PR; Neurocomputing; Cognitive Computation; Neural Computing and applications; 计算机学报; 自动化学报; ICRA2016; ICPR2016等.

论文专利

patent of the paper

[16] Guoqiang Zhong, Shoujun Yan, Kaizhu Huang, Yajuan Cai, Junyu Dong: Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification. Cognitive Computation (in press).
[J15] Xiaoyi Pan, Jing Wang, Xudong Zhang, Yuan Mei, Lu Shi, Guoqiang Zhong: A Deep Learning Model for the Amplitude Inversion of Internal Waves Based on Optical Remote Sensing Images. International Journal of Remote Sensing (in press).
[J14] Xiaopeng Liu, Guoqiang Zhong, Junyu Dong: Natural Image Illuminant Estimation via Deep Non-negative Matrix Factorisation. IET Image Processing (in press).
[J13] Qin Zhang, Hui Wang, Junyu Dong, Guoqiang Zhong, Xin Sun: Prediction of Sea Surface Temperature using Long ShortTerm Memory. IEEE Geoscience and Remote Sensing Letters (in press).
[J12] Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: SLMOML: Online Metric Learning with Global Convergence. IEEE Transactions on Circuits and Systems for Video Technology (in press).
[J11] Feng Gao, Xiaopeng Liu, Junyu Dong, Guoqiang Zhong, Muwei Jian: Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks. Remote Sensing 9 (5): 435 (2017).
[J10] Partha Pratim Roy, Guoqiang Zhong, Mohamed Cheriet: Tandem HMMs Using Deep Belief Networks for Offline Handwriting Recognition. Frontiers of Information Technology and Electronic Engineering 18(7): 978-988 (2017).
[J9] Jianyuan Sun, Guoqiang Zhong, Junyu Dong, Hina Saeeda, Qin Zhang: Cooperative Profit Random Forests With Application in Ocean Front Recognition. IEEE Access 5: 1398-1408 (2017)
[J8] Qin Zhang, Jianyuan Sun, Guoqiang Zhong, Junyu Dong: Random Multi-Graphs: A Semi-Supervised Learning Framework for Classification of High Dimensional Data. Image and Vision Computing 60: 30-37 (2017)
[J7] Xiaopeng Liu, Guoqiang Zhong, Cong Liu, Junyu Dong: Underwater Image Colour Constancy Based on Deep Sparse Nonnegative Matrix Factorization. IET Image Processing 11(1): 38-43 (2017)
[J6] Guoqiang Zhong, Li-Na Wang, Xiao Ling, Junyu Dong: An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning. The Journal of Finance and Data Science, 2(4): 265-278 (2016)
[J5] Heng Zhang, Guoqiang Zhong: Improving Short Text Classification by Learning Vector Representations of both Words and Hidden Topics. Knowl.-Based Syst. 102: 76-86 (2016)
[J4] Guoqiang Zhong, Mohamed Cheriet: Tensor representation learning based image patch analysis for text identification and recognition. Pattern Recognition 48(4) : 1207-1220 (2015).
[J3] Guoqiang Zhong, Mohamed Cheriet: Large margin low rank tensor analysis. Neural Computation 26(4) : 761-780 (2014).
[J2] Guoqiang Zhong, Cheng-Lin Liu: Error-correcting output codes based ensemble feature extraction. Pattern Recognition 46(4): 1091-1100 (2013).
[J1] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Joint learning of error-correcting output codes and dichotomizers from data. Neural Computing and Applications 21(4): 715-724 (2012).

[C25] Qin Zhang, Junyu Dong, Guoqiang Zhong: Visual Texture Perception via Graph-based Semi-supervised Learning. ICGIP, 2017.
[C24] Guoqiang Zhong, Hongxu Wei, Yuchen Zheng, Junyu Dong: Marginal Deep Architectures. ACPR, 2017.
[C23] Yanhai Gan, Huifang Chi, Ying Gao, Jun Liu, Guoqiang Zhong, Junyu Dong: Perception Driven Texture Generation. ICME, 2017.
[C22] Xiaowei Zhou, Guoqiang Zhong, Lin Qi, Junyu Dong,Tuan D. Pham, Jianzhou Mao: Surface Height Map Estimation from a Single Image Using Convolutional Neural Networks. ICGIP, 2016.
[C21] Xuecheng Han, Hui Yao, Guoqiang Zhong: Handwritten Text Line Segmentation by Spectral Clustering. ICGIP, 2016.
[C20] Guoqiang Zhong, Hui Yao, Yutong Liu, Chen Hong, Tuan Pham: Classification of Photographed Document Images Based on Deep-Learning Features. ICGIP, 2016.
[C19] Guoqiang Zhong, Xiao Ling: The necessary and sufficient conditions for the existence of the optimal solution of trace ratio problems. CCPR, 2016.
[C18] Guoqiang Zhong, Yaxin Shi, Mohamed Cheriet: Relational Fisher Analysis: A General Framework for Dimensionality Reduction. IJCNN, 2016
[C17] Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: Scalable Large Margin Online Metric Learning. IJCNN, 2016.
[C16] Guoqiang Zhong, Hui Xu, Pan Yang, Sijiang Wang, Junyu Dong: Deep Hashing Learning Networks. IJCNN, 2016.
[C15] Jianwen Lou, Lin Qi, Junyu Dong, Hui Yu, Guoqiang Zhong: Learning Perceptual Texture Similarity and Relative Attributes from Computational Features. IJCNN, 2016.
[C14] Pan Yang, Haoran Zhao, Lin Qi, Guoqiang Zhong: Self-taught recovery of depth data. APSIPA 2015: 1270-1275.
[C13] Yajuan Cai, Guoqiang Zhong, Yuchen Zheng, Kaizhu Huang, Junyu Dong: Is DeCAF Good Enough for Accurate Image Classification? ICONIP (2) 2015: 354-363.
[C12] Yuchen Zheng, Yajuan Cai, Guoqiang Zhong, Youssouf Chherawala, Yaxin Shi, Junyu Dong: Stretching deep architectures for text recognition. ICDAR 2015: 236-240.
[C11] Guoqiang Zhong, Xin Mao, Yaxin Shi, Junyu Dong: 3D Texture Recognition for RGB-D Images. CAIP (2) 2015: 518-528.
[C10] Chengcheng Jia, Guoqiang Zhong, Yun Raymond Fu: Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery. AAAI 2014: 1228-1234.
[C9] Yuchen Zheng, Guoqiang Zhong, Jun Liu, Xiaoxu Cai, Junyu Dong: Visual Texture Perception with Feature Learning Models and Deep Architectures. CCPR 2014.
[C8] Guoqiang Zhong, Mohamed Cheriet: An empirical evaluation of supervised dimensionality reduction for recognition. ICDAR 2013.
[C7] Guoqiang Zhong, Mohamed Cheriet: Adaptive error-correcting output codes. IJCAI 2013.
[C6] Guoqiang Zhong, Mohamed Cheriet: Image patches analysis for text block identification. ISSPA 2012: 1241-1246.
[C5] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Low rank metric learning with manifold regularization. ICDM 2011: 1266-1271.
[C4] Guoqiang Zhong, Wu-Jun Li, Dit-Yan Yeung, Xinwen Hou, Cheng-Lin Liu: Gaussian process latent random field. AAAI 2010.
[C3] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Learning ECOC and dichotomizers jointly from data. ICONIP (1) 2010: 494-502.
[C2] Guoqiang Zhong, Xinwen Hou, Cheng-Lin Liu: Relative distance-based Laplacian eigenmaps. CJKPR, 2009.
[C1] Guoqiang Zhong, Lina Wang, Dazhi Meng: The differential geometrical method of modifying SVM and its application in Intron-Extron classification. CCPR, 2007.

[BC3] Guoqiang Zhong, Mohamed Cheriet: Low Rank Tensor Manifold Learning. Low-Rank and Sparse Modeling for Visual Analysis, 2014.
[BC2] Mohamed Cheriet, Reza Farrahi Moghaddam, Ehsan Arabnejad Guoqiang Zhong: Manifold learning for the shape-based recognition of historical Arabic documents. Handbook of Statistics: Applications of Machine Learning, 2012.
[BC1] Guoqiang Zhong, Kaizhu Huang, Xinwen Hou, Shiming Xiang: Local tangent space Laplacian eigenmaps. Computational Intelligence, 2012.
Jx: 期刊 x; Cx: 会议 x; BCx: 书的章节 x.

研究生招生条件

postgraduate admissions conditions

现已开始招收2018级保研硕士研究生, 欢迎有志于从事人工智能、机器学习、模式识别、计算机视觉、大数据挖掘、机器人等相关方向的同学加入我的团队, 要求学生品质勤奋、踏实并具有良好的数学和英语运用能力和计算机编程能力, 联系邮箱: gqzhong@ouc.edu.cn.

备注

remarks

实验室个人主页: http://cvpr.ouc.edu.cn/people/zhonggq.html.

Call for book chapter: 由美国NOVA出版社出版, 书名"Semi-Supervised Learning: Background, Applications and Future Directions", 欢迎有相关工作的老师和我联系(gqzhong@ouc.edu.cn).