Abstract
Image classification is a well-known classical problem in multimedia content analysis. In this paper a framework of semi-supervised image classification method is presented based on random feature subspace. Firstly, color spatial distribution entropy is introduced to represent the color spatial information, and texture feature are extracted by using Gabor filter. Then random subspaces of the feature vector are dynamically generated from mixed feature vector as different views. Finally, three classifiers are trained by the classified images and tri-training algorithm is applied to classify sample images. Experimental results strongly demonstrate the effectiveness and robustness of the proposed system.
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
Preview
Unable to display preview. Download preview PDF.
References
Park, D.C.: Multiple feature-based classifier and its application to image classification. In: Proceedings of IEEE International Conference on Data Mining Workshops, pp. 65–71. IEEE Computer Society, Sydney (2010)
Promdaen, S., Wattuya, P., Sanevas, N.: Automated microalgae image classification. Procedia Computer Science 29, 1981–1992 (2014)
Zhang, L., Ma, C.: Low-rank decomposition and Laplacian group sparse coding for image classification. Neurocomputing 135, 339–347 (2014)
Rigamonti, R., Lepetit, V.: On the relevance of sparsity for image classification. Computer Vision and Image Understanding 125, 115–127 (2014)
Yang, J., Yu, K., Huang, T.: Supervised translation-invariant sparse coding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3517–3524 (2010)
Olshausen, B.A., et al.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Computer Vision and Image Understanding, 607–609 (1996)
Ando, R.K., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research 6, 1817–1853 (2005)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(2), 177–196 (2001)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. of the IEEE Conference on Machine Learning, Washington, USA, pp. 912–919 (2003)
Li, D.-X., Peng, J.-Y., Li, Z., Bu, Q.: LSA based multi-instance learning algorithm for image retrieval. Signal Process. 91(8), 1993–2000 (2001)
Zhou, Z.-H., Zhang, M.-L.: Multi-instance multi-label learning with application to scene classification. Advances in Neural Information Processing Systems 19, 1609–1616 (2007)
Zhou, Z.-H., Xu, J.-M.: On the relation between multi-instance learning and semi-supervised learning. In: Proceedings of the 24th ICML, Corvalis, Oregon, pp. 1167–1174 (June 2007)
Li, H., Wei, Y., Li, L., Yuan, Y.: Similarity learning for object recognition based on derived kernel. Neurocomputing 83, 110–120 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, L., Huaxiang, Z., Xiaojun, H., Feifei, S. (2014). Semi-supervised Image Classification Learning Based on Random Feature Subspace. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-662-45646-0_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
Online ISBN: 978-3-662-45646-0
eBook Packages: Computer ScienceComputer Science (R0)