Few-shot Object Recognition based on Three-Way Decision and Active Learning

Journal article

Li, B., Luo, S., Wang, J., Tian, L. and Chen, D. (2022). Few-shot Object Recognition based on Three-Way Decision and Active Learning. Visual Computer . 37.
AuthorsLi, B., Luo, S., Wang, J., Tian, L. and Chen, D.
Abstract

The problem of object recognition with few-shot or a few labeled samples is a very challenging research. Focusing on the problem of few shot object recognition, it is introduced a method of object recognition method based on active learning. To solve the recognition problem of unknown classes under the condition of few shot, based on the object recognition with active learning, we propose a sample selection method based on three-way decision. Firstly, we divide the unlabeled samples into three disjoint domains according to the recognition probability and the three-way decision threshold. Then, we use different query strategies for the three domains, as far as possible to select new samples of unknown classes from unlabeled samples, enrich the recognition types of the classification model, and optimize the performance of the classification model. Then, this paper introduces an algorithm for solving three-way decision threshold. Finally, simulation experiments verify the feasibility and effectiveness of the algorithm proposed in this chapter in object recognition.

Keywordsfew-shot object recognition; three-way decision; active learning; few-shot sample selection; object recognition
Year2022
JournalVisual Computer
Journal citation37
PublisherSpringer
ISSN0178-2789
Web address (URL)https://www.springer.com/journal/371
Publication dates
Print15 Feb 2022
Publication process dates
Accepted20 Jan 2022
Deposited31 Jan 2022
Publisher's version
File Access Level
Open
Accepted author manuscript
License
File Access Level
Open
Additional information

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/

Permalink -

https://openresearch.lsbu.ac.uk/item/8z1q7

Restricted files

Accepted author manuscript

  • 245
    total views
  • 7
    total downloads
  • 15
    views this month
  • 0
    downloads this month

Export as

Related outputs

Photothermal Radiometry Data Analysis by Using Machine Learning
Xiao, P. and Chen, D. (2024). Photothermal Radiometry Data Analysis by Using Machine Learning. Sensors. 24 (10), p. 3015. https://doi.org/10.3390/s24103015
Novel Parameter-Free and Parametric Same Degree Distribution-based Dimensionality Reduction Algorithms for Trustworthy Data Structure Preserving
Hajderanj, L., Chen, D. and Dudley-Mcevoy, S. (2023). Novel Parameter-Free and Parametric Same Degree Distribution-based Dimensionality Reduction Algorithms for Trustworthy Data Structure Preserving. Information Sciences. 661, p. 120030. https://doi.org/10.1016/j.ins.2023.120030
Skin Capacitive Image Stitching and Occlusion Measurements
Ciortea, L. I., Chen, D. and Xiao, P. (2023). Skin Capacitive Image Stitching and Occlusion Measurements. Cosmetics. 10 (1), p. 32. https://doi.org/10.3390/cosmetics10010032
Understanding Cancer Patients with Diagnostically Influential Factors using High Dimensional Data Embedding
Syed, A. S., Hajderanj, L., Guo, K. and Chen, D. (2022). Understanding Cancer Patients with Diagnostically Influential Factors using High Dimensional Data Embedding. in: Imoize, A. L., Hemanth, D. J., Do, D.-T. and Sur, S. N. (ed.) Explainable Artificial Intelligence in Medical Decision Support Systems The Institution of Engineering and Technology (IET).
UAV target tracking method based on deep reinforcement learning
Zhang, H., He, P., zhang, M., Chen, D., Neretin, E. and Li, B. (2022). UAV target tracking method based on deep reinforcement learning. 2022 International Conference on Cyber-physical Social Intelligence. Nanjing, China 21 - 24 Oct 2022
Developing Phoneme-based Lip-reading Sentences System for Silent Speech Recognition
El Bialy, R., Chen, D., Fenghour, S., Hussein, W., Xiao, P., Karam, O. H. and Li, B. (2022). Developing Phoneme-based Lip-reading Sentences System for Silent Speech Recognition. CAAI Transactions on Intelligence Technology. 8 (1), pp. 128-139. https://doi.org/10.1049/cit2.12131
An effective context-focused hierarchical mechanism for task-oriented dialogue response generation
Zhao, M., Wang, L., Jiang, Z., Li, R., Lu, X., Hu, Z. and Chen, D. (2022). An effective context-focused hierarchical mechanism for task-oriented dialogue response generation. Computational Intelligence. 38 (5), pp. 1831-1858. https://doi.org/10.1111/coin.12544
The Effect of Sun Tan Lotion on Skin By Using Skin TEWL and Skin Water Content Measurements
Xiao, P. and Chen, D. (2022). The Effect of Sun Tan Lotion on Skin By Using Skin TEWL and Skin Water Content Measurements. MDPI Sensors. 22. https://doi.org/10.3390/s22093595
An Effective Conversion of Visemes to Words for High-Performance Automatic Lipreading.
Fenghour, S., Chen, D., Guo, K., Li, B. and Xiao, P. (2021). An Effective Conversion of Visemes to Words for High-Performance Automatic Lipreading. Sensors. 21 (23). https://doi.org/s21237890
UAV visual flight control method based on deep reinforcement learning
Bai, S., Li, B., Gan, Z. and Chen, D. (2021). UAV visual flight control method based on deep reinforcement learning. 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI). https://doi.org/10.1109/iccsi53130.2021.9736242
UAV flight control method based on deep reinforcement learning
Bai, S., Li, B., Gan, Z. and Chen, D. (2021). UAV flight control method based on deep reinforcement learning. 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI). Beijing, China 18 Dec 2021 - 20 Mar 2022 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCSI53130.2021.9736242
ME‐MADDPG: An efficient learning‐based motion planning method for multiple agents in complex environments
Wan, K., Wu, D., Li, B., Gao, X., Hu, Z. and Chen, D. (2021). ME‐MADDPG: An efficient learning‐based motion planning method for multiple agents in complex environments. International Journal of Intelligent Systems. 37 (3), pp. 2393-2427. https://doi.org/10.1002/int.22778
Learning the structure of Bayesian networks with ancestral and/or heuristic partition
Tan, X., Gao, X., Wang, Z., Han, H., Liu, X. and Chen, D. (2021). Learning the structure of Bayesian networks with ancestral and/or heuristic partition. Information Sciences. https://doi.org/10.1016/j.ins.2021.10.052
Deep Learning-based Automated Lip-Reading: A Survey
Fenghour, S., Chen, D., Guo, K., Li, B. and Xiao, P. (2021). Deep Learning-based Automated Lip-Reading: A Survey. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3107946
Deep Learning Causal Attributions of Breast Cancer
Chen, D., Hajderanj, L., Mallet, S., Camenen, P., Li, B., Hao, R. and Zhao, E. (2021). Deep Learning Causal Attributions of Breast Cancer. in: Arai, K. (ed.) Intelligent Computing, Proceedings of the 2021 Computing Conference, Lecture Notes in Networks and Systems, Vol 285, Intelligent Computing (A. Kohei, Editor) Springer.
The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study
Hajderanj, L., Chen, D. and Weheliye, I. (2021). The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study. IEEE Access. 9. https://doi.org/10.1109/ACCESS.2021.3066259
Enhancing Transformer-based language models with Commonsense Representations for Knowledge-driven Machine Comprehension
Li, R., Jiang, Z., Wang, L., Lu, X., Zhao, M. and Chen, D. (2021). Enhancing Transformer-based language models with Commonsense Representations for Knowledge-driven Machine Comprehension. Knowledge-Based Systems. 220, p. 106936. https://doi.org/10.1016/j.knosys.2021.106936
The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms
Xiao, P., Zhang, Xu, Pan, Wei, Ou, Xiang, Bontozoglou, C., Chirikhina, E. and Chen, D. (2020). The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms. Cosmetics. 7 (3), p. e67. https://doi.org/10.3390/cosmetics7030067
Three‐way decision of target threat decision making based on adaptive threshold algorithms
Li, B., Tian, Li., Han, Y. and Chen, D. (2020). Three‐way decision of target threat decision making based on adaptive threshold algorithms. The Journal of Engineering. 2020 (13), pp. 293-297. https://doi.org/10.1049/joe.2019.1202
Effectiveness analysis of ship formation air defence based on deep belief network
Li, B., Luo, H., Wang, Y. and Chen, D. (2020). Effectiveness analysis of ship formation air defence based on deep belief network. The Journal of Engineering. 2020 (13), pp. 394-398. https://doi.org/10.1049/joe.2019.1201
Maneuvering target tracking of UAV based on MN-DDPG and transfer learning
Li, B., Yang, Z.P., Chen, D.Q., Liang, S.Y. and Ma, H. (2020). Maneuvering target tracking of UAV based on MN-DDPG and transfer learning. Defence Technology. https://doi.org/10.1016/j.dt.2020.11.014
Lip Reading Sentences Using Deep Learning with Only Visual Cues
Fenghour, S., Chen, D., Guo, K. and Xiao, P. (2020). Lip Reading Sentences Using Deep Learning with Only Visual Cues. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3040906
Deep Learning Causal Attributions of Breast Cancer
Chen, D, Hajderanj, L, Mallet, S, Camenen, P, Li, B, Ren, H and Zhao, E (2020). Deep Learning Causal Attributions of Breast Cancer. Computing 2021. London 15 - 16 Jul 2021 The Science and Information (SAI) Organization. https://doi.org/10.1007/978-3-030-80129-8_10
UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning
Li, B., Gan, Z., Chen, D. and Aleksandrovich, D.S. (2020). UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning. Remote Sensing. 12 (22), p. 3789. https://doi.org/10.3390/rs12223789
Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing
Hajderanj, L., Chen, D., Grisan, E. and Dudley-McEvoy, S (2020). Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing. IEEE Access. 8, pp. 207141 - 207155. https://doi.org/10.1109/ACCESS.2020.3038460
Learning Bayesian Networks based on Order Graph with Ancestral Constraints
Wang, Z., Gao, X., Tian, X., Yang, Y. and Chen, D. (2020). Learning Bayesian Networks based on Order Graph with Ancestral Constraints. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2020.106515
An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration
Li, B., Liang, S., Tian, L., Chen, D. and Zhang, M. (2020). An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration. Mathematical Problems in Engineering. p. 5391479. https://doi.org/10.1155/2020/5391479
An adaptive dwell time scheduling model for phased array radar based on three-way decision
Li, B., Tian, L., Chen, D. and Liang, S. (2020). An adaptive dwell time scheduling model for phased array radar based on three-way decision. Journal of Systems Engineering and Electronics. pp. 500-509. https://doi.org/10.23919/JSEE.2020.000030
Intelligent Aircraft Maneuvering Decision Based on CNN
Li, B., Liang, S., Tian, L. and Chen, D. (2019). Intelligent Aircraft Maneuvering Decision Based on CNN. Proceedings of the 3rd International Conference on Computer Science and Application Engineering. (138). https://doi.org/10.1145/3331453.3362046
Intelligent Attitude Control of Aircraft Based on LSTM
Li, B, Gao, P, Li, X and Chen, D (2019). Intelligent Attitude Control of Aircraft Based on LSTM. 3rd International Conference on Artificial Intelligence Applications and Technologies. Beijing, China 01 - 03 Aug 2019 IOP Publishing. https://doi.org/10.1088/1757-899X/646/1/012013
A Task Scheduling Algorithm for Phased Array Radar Based on Dynamic Three-way Decision
Li, B., Tian, L., Chen, D. and Han, Y. (2019). A Task Scheduling Algorithm for Phased Array Radar Based on Dynamic Three-way Decision. Sensors. 20 (1). https://doi.org/10.3390/s20010153
Intelligent Flight Control of Combat Aircraft Based on Autoencoder
Li, B., Gao, P., Liang, S. and Chen, D. (2019). Intelligent Flight Control of Combat Aircraft Based on Autoencoder. 2019 The 4th International Conference on Robotics, Control and Automation. GuangZhou 26 - 28 Jul 2019 https://doi.org/10.1145/3351180.3351210
Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet
Zhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D. and Xiao, P. (2019). Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet. Computing Conference 2020. London, UK 16 - 17 Jul 2019 Springer.
FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction
Wang, L.F., Lu, X., Jiang, Z., Zhang, Z., Li, R., Zhao, M. and Chen, D. (2019). FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction. Mathematical Biosciences and Engineering. 16 (6), pp. 7789-7807. https://doi.org/10.3934/mbe.2019391
Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study
Chen, D., Guo, K. and Li, B. (2019). Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study. 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019). Havana, Cuba 28 - 31 Oct 2019 https://doi.org/10.1007/978-3-030-33904-3_16
Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method
Yang, Y, Gao, X, Guo, Z and Chen, D (2019). Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method. Pattern Recognition. 91, pp. 123-134. https://doi.org/10.1016/j.patcog.2019.02.006
Learning Bayesian network parameters via minimax algorithm
Gao, X, Gao, G, Ren, H, Chen, D and He, C (2019). Learning Bayesian network parameters via minimax algorithm. International Journal of Approximate Reasoning. 108, pp. 62-75. https://doi.org/10.1016/j.ijar.2019.03.001
Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models
Adegoke, V, Chen, D and Banissi, E (2019). Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models. International Journal of Computer Information Systems and Industrial Management. 11, pp. 82-100.
A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification
Hajderanj, L, Weheliye, I and Chen, D (2019). A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification. 2019 8th International Conference on Software and Information Engineering. Cairo 09 - 12 Apr 2019
Recurrent Neural Networks for Decoding Lip Read Speech
Fenghour, S, Chen, D and Xiao, P (2019). Recurrent Neural Networks for Decoding Lip Read Speech. 2019 8th International Conference on Software and Information Engineering (ICSIE 2019). Cairo 09 - 12 Apr 2019
Decoder-Encoder LSTM for Lip Reading
Fenghour, S., Chen, D. and Xiao, P. (2019). Decoder-Encoder LSTM for Lip Reading. Proceedings of the 2019 8th International Conference on Software and Information Engineering. https://doi.org/10.1145/3328833.3328845
Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition
Adegoke, V, Chen, D, Banissi, E and Barikzai, S (2019). Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition. The 10th International Conference on Soft Computing and Pattern Recognition. Porto, Portugal 13 - 15 Dec 2018
Distributed deep networks based on Bagging-Down SGD algorithm
Qin, C, Gao, X and Chen, D (2019). Distributed deep networks based on Bagging-Down SGD algorithm. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics. 41 (5), pp. 1021-1027. https://doi.org/10.3969/j.issn.1001-506X.2019.05.13
Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach
Chen, D, Hajderanj, L and Fiske, J (2019). Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach. 13th Multi Conference on Computer Science and Information Systems (MCCSIS). Porto, Portugal 16 - 18 Jul 2019
Design of a voice control 6DoF grasping robotic arm based on ultrasonic sensor, computer vision and Alexa voice assistance
Wang, Z, Chen, D and Xiao, P (2019). Design of a voice control 6DoF grasping robotic arm based on ultrasonic sensor, computer vision and Alexa voice assistance. International Conference on Information Technology in Medicine and Education. Qingdao, China 23 - 25 Aug 2019 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ITME.2019.00150
Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards
Chen, D, Sanz, BM and Zhao, E (2018). Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards. International Conference on Big Data Analytics, Data Mining and Computational Intelligence 2018 (BigDaCI 2018). Madrid, Spain 18 - 20 Jul 2018 Bigdaci.
Contour Mapping for Speaker-Independent Lip Reading System
Fenghour, S, Chen, D and Xiao, P (2018). Contour Mapping for Speaker-Independent Lip Reading System. The 11th International Conference on Machine Vision (ICMV 2018). Munich, Germany 01 - 03 Nov 2018
Learning Bayesian Network Parameters from a Small Data Set: A Further Constrained Qualitatively Maximum a Posteriori Method
Guo, Zhi-gao, Gao, Xiao-guang, Hao, Ren, Yang, Yu, Di, Ruo-hai and Chen, D (2017). Learning Bayesian Network Parameters from a Small Data Set: A Further Constrained Qualitatively Maximum a Posteriori Method. International Journal of Approximate Reasoning. 91 (Dec), pp. 22-35. https://doi.org/10.1016/j.ijar.2017.08.009
Feature Extraction and Labelling Large Data Sets Using Deep Learning
Chen, D (2017). Feature Extraction and Labelling Large Data Sets Using Deep Learning. RESEARCHER LINK: Smart Technology for Fighting Virus Epidemics & Bioinformatics. Recife, Pernambuco, Brazil 10 - 13 Sep 2017
Prediction of Breast Cancer Survivability using Ensemble Algorithms
Adegoke, V, Chen, D, Banissi, E and Barikzai, S (2017). Prediction of Breast Cancer Survivability using Ensemble Algorithms. International Conference on Smart System and Technologies 2017 (SST 2017),. Osijek, Croatia 18 - 20 Oct 2017
Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods
Adegoke, V, Chen, D, Barikzai, S and Banissi, E (2017). Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods. 2017 European Modelling Symposium (EMS). Manchester 20 - 21 Nov 2017
Making Better Use of Big Data
Chen, D (2016). Making Better Use of Big Data. LSBU Enterprise Count Event, March 2016. London Southbank University 18 - 18 Mar 2016 London South Bank University.
Big Data Analytics In The Public Sector: A Case Study Of NEET Analysis For The London Boroughs
Chen, D, Asaolu, B and Qin, C (2016). Big Data Analytics In The Public Sector: A Case Study Of NEET Analysis For The London Boroughs. International Conference on Big Data Analytics, Data Mining and Computational Intelligence. Funchal, Madeira, Portugal 02 - 04 Jul 2016
On Distributed Deep Network for Processing Large-Scale Sets of Complex Data
Qin, C, Gao, X and Chen, D (2016). On Distributed Deep Network for Processing Large-Scale Sets of Complex Data. 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Hangzhou, China. 27 - 28 Aug 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IHMSC.2016.55
A Bayesian Approach to Learn Bayesian Networks Using Data and Constraints
Gao, X, Yu, Y, Zhi-gao, G and Chen, D (2016). A Bayesian Approach to Learn Bayesian Networks Using Data and Constraints. 23rd International Conference on Pattern Recognition (ICPR 2016). Cancún, México 04 - 08 Dec 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICPR.2016.7900204
Big Data Analytics System for Fact/Data-driven Decision Making
Chen, D (2015). Big Data Analytics System for Fact/Data-driven Decision Making. The Royal Statistical Society, Business and Industry Section. London, UK 18 Nov 2015 Royal Statistical Society .
Determining Key (Predictor) Modules for Early Identification of Students At-Risk
Chen, D and Elliott, G (2013). Determining Key (Predictor) Modules for Early Identification of Students At-Risk. International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013). Beijing, China 19 - 20 Dec 2013 Atlantis Press. https://doi.org/10.2991/icaiees-13.2013.22
Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining
Chen, D (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management. 19 (3), pp. 197-208. https://doi.org/10.1057/dbm.2012.17