December 4th - 7th 2021, Orlando, Florida, USA
Computer vision is a big research field that studies to use computers process, extract, analyse and understand information from digital images and videos as the human vision system does. It covers a wide range of applications in many important fields, including engineering, biology, medicine, remote sensing, and business. Furthermore, many computer vision tasks are highly related to our daily life, from face detection in the mobile phone to self-driving vehicles. Typical tasks related to computer vision and image analysis include image processing, edge detection, image classification, image segmentation, object detection, scene analysis, biological identification, motion analysis, and image restoration. These tasks have not been comprehensively solved, particularly in the era of big data, when image data are easy to obtained but may be more challenging to analyse. It is necessary to develop new effective and efficient methods to solve the tasks in computer vision.
Evolutionary computation is a sub-field of artificial intelligence that includes a family of nature-inspired population-based algorithms/techniques. Evolutionary computation techniques have promising global search/optimization ability to find high-quality solutions to complex problems without requiring rich domain knowledge. Existing evolutionary computation paradigms include Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential Evolution (DE), Evolutionary Multi-objective Optimization (EMO) and Memetic Computing (MC). Evolutionary computation techniques have been successfully applied to solve many computer vision and image analysis tasks, including image classification, image segmentation, object recognition, and image registration. However, the potential of evolutionary computation has not been comprehensively investigated in computer vision and image analysis. The challenges of improving effectiveness, efficiency, and interpretability, and reducing the requirement of domain knowledge and human intervention are urging to be addressed by investigating new evolutionary computation approaches to computer vision and image analysis.
This special session aims to investigate the use of evolutionary computation for computer vision and image analysis, covering ALL different evolutionary computation paradigms and their applications to computer vision and image analysis. It will bring together researchers and practitioners from around the world to discuss the latest advances in the field and will act as a major forum for the presentation of recent research. Authors are invited to submit their original and unpublished work to this special session. Topics related to all aspects of evolutionary computation for computer vision and image analysis, such as theories, algorithms, systems and applications, are welcome.Topics of interest include but are not limited to:
Ying Bi School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Ying.Bi@.vuw.ac.nz Phone: +64-4-463 5233; Fax: +64-4-463 5045.
Bing Xue School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Bing.Xue@ecs.vuw.ac.nz Phone: +64-4-463 5542; Fax: +64-4-463 5045.
Mengjie Zhang School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand. Mengjie.Zhang@ecs.vuw.ac.nz Phone: +64-4-463 5654; Fax: +64-4-463 5045
Ying Bi is currently a postdoctoral research fellow in artificial intelligence with School of Engineering and Computer Science at Victoria University of Wellington (VUW). Her research focuses mainly on computer vision, image analysis, machine learning, evolutionary computation, classification, feature learning, and transfer learning. She has published an authored book and over 30 papers in fully refereed journals and conferences in computer vision and evolutionary computation. She has been serving as an organizing committee member of IEEE CEC 2019 and Australasian AI 2018 and a program committee member of over ten international conferences including IJCAI, GECCO, IEEE CEC, IEEE SSCI, and Australian AI. She is co-chair Poster session in IEEE CEC 2019. She is serving as a reviewer of over ten international journals including IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems, IEEE Computational Intelligence Magazine, and IEEE Transactions on Artificial Intelligence.
She is a member of IEEE, IEEE Computational Intelligence Society (CIS) and ACM SIGEVO.
Bing Xue is currently a Professor of Artificial Intelligence and Program Director of Science in the School of Engineering and Computer Science at VUW. She has over 200 papers published in fully refereed international journals and conferences and her research focuses mainly on evolutionary computation, data mining, machine learning, classification, symbolic regression, feature selection, evolving deep neural networks, image analysis, transfer learning, multi-objective machine learning. Dr Xue is currently the Chair of IEEE CIS Task Force on Transfer Learning & Transfer Optimization, and Vice-Chair of IEEE Task Force on Evolutionary Feature Selection and Construction and of IEEE CIS Task Force on Evolutionary Deep Learning and Applications. She was the Chair of IEEE Computational Intelligence Society (CIS) Data Mining and Big Data Analytics Technical Committee.
Prof Xue is the organizer of the special session on Evolutionary Feature Selection and Construction in IEEE Congress on Evolutionary Computation (CEC) 2015, 2016, 2017, 2018, 2019, 2020, and 2021. Prof Xue has been a chair for a number of international conferences including the Chair of Women@GECCO 2018, a co-Chair of the Evolutionary Machine Learning Track for GECCO 2019 and 2020, a co-Chair of the first Neuroevolution Track for GECCO 2021. She is the Lead Chair of IEEE Symposium on Computational Intelligence in Feature Analysis, Selection, and Learning in Image and Pattern Recognition (FASLIP) at SSCI 2016, 2017, 2018, 2019 and 2020, a Program Co-Chair of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), a Program Chair of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Finance Chair of 2019 IEEE Congress on Evolutionary Computation, a Workshop Chair of 2021 IEEE International Conference on Data Mining (ICDM), a Conference Activities Chair of 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), a General Co-Chair of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ 2020), a Tutorial Co-Chair of 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), and a Publication Co-Chair of Proceedings of the 25th European Conference on Genetic Programming (EuroGP 2022).
She is an Editor of the IEEE Computational Intelligence Society Newsletter. She is an Associate Editor or Member of the Editorial Board for over ten international journals, including IEEE Transactions on Evolutionary Computation, IEEE Computational Intelligence Magazine, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, and ACM Transactions on Evolutionary Learning and Optimization.
Prof. Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, an IEEE CIS Distinguished Lecturer, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.
His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimization and learning classifier systems with application areas of feature selection/construction and dimensionality reduction, computer vision and image processing, evolutionary deep learning and transfer learning, job shop scheduling, multi-objective optimization, and clustering and classification with unbalanced and missing data. He is also interested in data mining, machine learning, and web information extraction. Prof Zhang has published over 700 research papers in refereed international journals and conferences in these areas.
He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), ACM Transactions on Evolutionary Learning and Optimization, Genetic Programming and Evolvable Machines (Springer), IEEE Transactions on Emergent Topics in Computational Intelligence, Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for eight international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten world genetic programming researchers by the GP bibliography (http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html).
Prof Zhang was the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020 and GECCO 2021. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). Since 2014, he has been co-organizing and co-chairing the special session on evolutionary feature selection and construction at IEEE CEC and SEAL, and also delivered a keynote/plenary talk for IEEE CEC 2018, IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017. Prof. Zhang is the Chair of IEEE CIS PhD Dissertation Award Committee and IEEE CIS Publication Strategic Planning Committee. He was the Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee, and the IEEE CIS Evolutionary Computation Technical Committee; a Vice-Chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the IEEE CIS Task Force on Evolutionary Deep Learning and Applications; and also the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.