Abstract
Aiming at the low accuracy and poor applicability of traditional SVM classifiers, this paper proposes an image classification system based on MKL-MKB (multi kernel learning-multi kernel boosting). This approach firstly integrates existing feature extraction methods to extract features like wavelet, Gabor, GLCM and so on. A weak classifier is constructed by using a synthetic kernel in kernel space. We use Nystrom approximation algorithm to calculate weights of kernel matrixes of multi-kernel model. Then we make a decision level fusion of weak classifiers under Adaboost framework to impair weights of weak kernels. Finally, experiments are carried out to verify the validity and applicability of the proposed algorithm by testing on terrain remote sensing images and several UCI data sets.
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References
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. (IJCV) 57(2), 137–154 (2004)
Shunmugapriya, P., Kanmani S., Prasath, B.S., et al.: Classifier ensembles using boosting with mixed learner models (BMLM). In: 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, pp. 151–155 (2011)
Shen, C., Hao, Z.: A direct formulation for totally-corrective multi-class boosting. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp. 2585–2592 (2011)
Ban, K.D., Yoon, Y., Yoon, H.S., et al.: Number detection in natural image with boosting classifier. In: International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 525–526 (2012)
Akbari, F., Sajedi, H.: SMS spam detection using selected text features and boosting classifiers. In: Information and Knowledge Technology. IEEE (2015)
Shahid, N., Naqvi, I.H., Qaisar, S.B.: Quarter-sphere SVM: attribute and spatio-temporal correlations based outlier & event detection in wireless sensor networks. In: IEEE Wireless Communications & Networking Conference. IEEE, pp. 2048–2053 (2012)
Sahri, Z., Yusof, R.: Fault diagnosis of power transformer using optimally selected DGA features and SVM. In: Control Conference. IEEE (2015)
Niranjan, S., Shin, Y.C.: Sparse multiple kernel learning for signal processing applications. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 788–798 (2010)
Close, R., Wilson, J., Gader, P.: A Bayesian approach to localized multi-kernel learning using the relevance vector machine. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1103–1106 (2011)
Lanckriet, G.R.G., Tijl, D.B., Nello, C., et al.: A statistical framework for genomic data fusion. Bioinformatics 20(16), 2626–2635 (2004)
Chen, T.T., Liu, C.J., Zou, H.L., et al.: A multi-instance multi-label scene classification method based on multi-kernel fusion. In: Sai Intelligent Systems Conference. IEEE (2015)
Zhou, Y., Cui, X., Hu, Q., et al.: Improved multi-kernel SVM for multi-modal and imbalanced dialogue act classification. In: International Joint Conference on Neural Networks. IEEE (2015)
Wang, Q., Gu, Y., Tuia, D.: Discriminative multiple Kernel learning for hyperspectral image classification. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) (2016). doi:10.1109/TGRS.2016.2530807
Bi, X., Pun, C.M., Yuan, X.C.: Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf. Sci. 345, 226–242 (2016)
Damoulas, T., Girolami, M.A.: Pattern recognition with a Bayesian kernel combination machine. Pattern Recogn. Lett. 30(1), 46–54 (2009)
Lee, K.M.: Locality-sensitive hashing techniques for nearest neighbor search. Int. J. Fuzzy Logic Intell. Syst. 12(4), 300–307 (2012)
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© 2016 Springer Science+Business Media Singapore
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Li, N., Huai, W., Gong, G. (2016). A MKL-MKB Image Classification Algorithm Based on Multi-kernel Boosting Method. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_17
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DOI: https://doi.org/10.1007/978-981-10-2663-8_17
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