Abstract
Image classification is an important issue in pattern recognition, the high dimension features is a challenging task since only a few number of them are effective in classification. To improve the classification efficiency, it is necessary to reduce the dimensionality of image features before classification. This study provides a novel image classification application based on Bacterial Colony Optimization, which can decreases the computation burden and improves the classification's efficiency. Specifically, the elimination strategy in original algorithm is removed, and the communication, chemotaxis, migration, and reproduction strategies are kept. Additionally, the communication and chemotaxis step size of the Bacterial Colony Optimization are modified for feature selection in image classification. Several comparision experiments on two public image datasets are conducted to verify the effectiveness of the method. Experimental results prove that the method can greatly improve the classification accuracy and efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, X., Wang, W., Li, Y., Jiao, L.C.: PSO-based automatic relevance determination and feature selection system for hyperspectral image classification. Electron. Lett. 48, 1263–1265 (2012)
Alizadeh Naeini, A., Babadi, M., Mirzadeh, S.M.J., Amini, S.: Particle Swarm optimization for object-based feature selection of VHSR satellite images. IEEE Geosci. Remote Sens. Lett. 15, 379–383 (2018)
Chen, L., Chen, B., Chen, Y.: Image feature selection based on ant colonny optimization. Aust. Joint Conf. Artif. Intell. 7106, 580–589 (2011)
Chen, B., Chen, L., Chen, Y.: Efficient ant colony optimization for image feature selection. Signal Process. 93, 1566–1576 (2013)
Panda, R., Kumar, M., Panigrahi, B.K.: Face recognition using bacterial foraging strategy. Swarm Evol. Comput. 1, 138–146 (2011)
Lei, L., Peng, J., Yang, B.: Image feature selection based on genetic algorithm. In: Zhong, Z. (ed.) Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. LNEE, vol. 219, pp. 825–831. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4853-1_101
Mlakar, U., Fister, I., Brest, J., Potočnik, B.: Multi-objective differential evolution for feature selection in facial expression recognition systems. Expert Syst. Appl. 89, 129–137 (2017)
Niu, B., Wang, H.: Bacterial colony optimization. Discret. Dyn. Nat. Soc. 2012, 1–28 (2012)
Wang, Y., Li, S., Mao, J.: Computer image processing and recognition technology. J. China High Education Press, pp. 31–105 (2001)
Haralick, R.M., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22, 52–67 (2002)
Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning (2000)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997)
He, Y., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69, 1493–1504 (2020)
Acknowledgement
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71901152, 61703102), Natural Science Foundation of Guangdong Province (2020A1515010752), Guangdong Basic and Applied Basic Research Foundation (Project No. 2019A1515011392), and Natural Science Foundation of Shenzhen University (85303/00000155).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, H., Zhou, Z., Wang, Y., Yan, X. (2021). Feature Selection for Image Classification Based on Bacterial Colony Optimization. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-78811-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78810-0
Online ISBN: 978-3-030-78811-7
eBook Packages: Computer ScienceComputer Science (R0)