Abstract
Image classification is the significant problems of concern in image processing and image recognition. There are many methods have been proposed for solving image classification problem such as k nearest neighbor (K-NN), Bayesian Network, Adaptive boost (Adaboost), Artificial Neural Network (NN), and Support Vector Machine (SVM). The aim of this paper is to propose a novel model using multi SVMs concurrently to apply for image classification. Firstly, each image is extracted to many feature vectors. Each of feature vectors is classified into the responsive class by one SVM. Finally, all the classify results of SVM are combined to give the final result. Our proposal classification model uses many SVMs. Let it call multi_SVM. As a case study for validation the proposal model, experiment trials were done of Oxford Flower Dataset divided into three categories (lotus, rose, and daisy) has been reported and compared on RGB and HIS color spaces. Results based on the proposed model are found encouraging in term of flower image classification accuracy.
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Yang, Z., Rongyi, H., Muwei, J.: Comparison of Two Methods for Texture Image Classification. In: Second International Workshop on Computer Science and Engineering, WCSE 2009, vol. 1, pp. 65–68. IEEE Press (2009)
Linlin, S., Li, B., Picton, P.: Facial recognition/verification using Gabor wavelets and kernel methods. In: International Conference on Image Processing, ICIP 2004, vol. 3, pp. 1433–1436. IEEE Press (2004)
White, K.P., Kundu, B., Mastrangelo, C.M.: Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation. IEEE Transactions on Semiconductor Manufacturing 21(2), 272–278 (2008)
Zhao, L., Guo, Z.: Face Recognition Method Based on Adaptively Weighted Block-Two Dimensional Principal Component Analysis. In: Third International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp. 22–25. IEEE Press (2011)
Xingfu, Z., Xiangmin, R.: Two Dimensional Principal Component Analysis based Independent Component Analysis for face recognition. In: International Conference on Multimedia Technology (ICMT), pp. 934–936. IEEE Press (2011)
Wakin, M.B.: Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity. Signal Processing Magazine 28(5), 144–146 (2011)
McSherry, D., Stretch, C.: An Analysis of Order Dependence in k-NN. In: Coyle, L., Freyne, J. (eds.) AICS 2009. LNCS, vol. 6206, pp. 207–218. Springer, Heidelberg (2010)
Madden, M.G.: A New Bayesian Network Structure for Classification Tasks. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds.) AICS 2002. LNCS (LNAI), vol. 2464, p. 203. Springer, Heidelberg (2002)
Li, S., Zhu, L., Jiang, T.-Z.: Active Shape Model Segmentation Using Local Edge Structures and AdaBoost. In: Yang, G.Z., Jiang, T.-Z. (eds.) MIAR 2004. LNCS, vol. 3150, pp. 121–128. Springer, Heidelberg (2004)
Yong, L., Xin, Y.: Negatively correlated neural networks for classification. Artificial Life and Robotics 3(4), 255–259 (1999)
Rud, S., Yang, J.-S.: A Support Vector Machine (SVM) Classification Approach to Heart Murmur Detection. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part II. LNCS, vol. 6064, pp. 52–59. Springer, Heidelberg (2010)
Agrawal, S., Verma, N.K., Tamrakar, P., Sircar, P.: Content Based Color Image Classification using SVM. In: Eighth International Conference on Information Technology: New Generations (ITNG), pp. 1090–1094. IEEE Press (2011)
Devis, T., Jordi, M., Mikhail, K., Gustavo, C.V.: Structured Output SVM for Remote Sensing Image Classification. Journal of Signal Processing Systems 65(3), 301–310 (2011)
Demir, B., Erturk, S.: Improving SVM classification accuracy using a hierarchical approach for hyperspectral images. In: 16th IEEE International Conference on Image Processing (ICIP), pp. 2848–2852. IEEE Press (2009)
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Le, T.H., Tran, H.S., Nguyen, T.T. (2013). Applying Multi Support Vector Machine for Flower Image Classification. In: Vinh, P.C., Hung, N.M., Tung, N.T., Suzuki, J. (eds) Context-Aware Systems and Applications. ICCASA 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36642-0_27
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DOI: https://doi.org/10.1007/978-3-642-36642-0_27
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