基于稀疏自动编码器和支持向量机的图像分类

doi:10.16182/j.issn1004731x.joss.201808023

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (8): 3007-3014.doi: 10.16182/j.issn1004731x.joss.201808023

基于稀疏自动编码器和支持向量机的图像分类

刘芳, 路丽霞, 王洪娟, 王鑫

北京工业大学信息与通信工程学院,北京 100124

收稿日期:2017-01-04 出版日期:2018-08-10 发布日期:2019-01-08
作者简介:刘芳(1971-),女,陕西西安,博士,副教授,研究方向为计算机智能和可视导航等;路丽霞(1992-),女,山东济宁,硕士,研究方向为图像处理。
基金资助:
国家自然科学基金(61171119),北京工业大学研究生科技基金(ykj-2015-12083)

Image Classification Based on Sparse Autoencoder and Support Vector Machine

Liu Fang, Lu Lixia, Wang Hongjuan, Wang Xin

College of Information and Communication Engineering, Beijing University of Technology, Beijing 100124, China

Received:2017-01-04 Online:2018-08-10 Published:2019-01-08

摘要/Abstract

摘要： 针对单层稀疏自动编码器在特征学习时容易丢失深层抽象特征,特征缺乏鲁棒性的缺点,提出一种新的基于稀疏自动编码器和支持向量机的图像分类方法。构建深度稀疏自动编码器对图像逐层学习并自动提取每层特征,根据特征集权值重组法得到每层特征权值和重组特征集。将遗传算法强大的全局搜索能力和支持向量机分类优势结合,高效、准确的完成图像分类。实验结果表明,该算法能自动地学习图像深层特征,重组特征集具有较高的特征识别力,有效地提高了图像分类准确率。

关键词: 稀疏自动编码器, 特征学习, 遗传算法, 支持向量机, 图像分类

Abstract: A new algorithm of image classification based on the sparse autoencoder and the support vector machine was proposed in view of the drawbacks that the single layer sparse autoencoder for feature learning is easy to lose the deep abstract feature and the features lack the robustness. The deep sparse autoencoder is constructed to learn each image layer and the feature of each layer is automatically extracted. The each feature weights and the reorganized set of feature are obtained according to the feature weighting method. By combining the strong global search ability of genetic algorithm and the excellent performance of support vector machine, the image classification is completed efficiently and accurately. The experimental results show that the proposed algorithm can automatically learn the deep feature of the image, and the reorganized feature has high feature discrimination ability, which effectively improves the accuracy of image classification.

Key words: sparse autoencoder, feature learning, genetic algorithm, support vector machine, image classification

中图分类号:

TP391.41

刘芳, 路丽霞, 王洪娟, 王鑫. 基于稀疏自动编码器和支持向量机的图像分类[J]. 系统仿真学报, 2018, 30(8): 3007-3014.

Liu Fang, Lu Lixia, Wang Hongjuan, Wang Xin. Image Classification Based on Sparse Autoencoder and Support Vector Machine[J]. Journal of System Simulation, 2018, 30(8): 3007-3014.

参考文献

[1] Xiong M, Chen J, Wang Z, et al.Deep Feature Representation via Multiple Stack AutoEnco-ders[M]. Advances in Multimedia Information Processing-PCM, 2015: 275-284.
[2] Chen Kunjin, Hu Jun, He Jinliang, et al.A Framework for Automatically Extracting Overvoltage Features Based on Sparse Autoencoder[J]. IEEE Transactions on Smart Grid (S1949-3053), 2016: 1-1.
[3] Tao Chao, Pan Hongbo, Li Yansheng, et al.Unsupervised Spectral-Spatial Feature Learning With Stacked Sparse Autoencoder for Hyprspectral Imagery Classification[J]. IEEE Geoscience and Remote Sensing Letters (S1545-598X). 2015: 2438-2442.
[4] Yin Hongpeng, Jiao Xuguo, Chai Yi, et al.Scene classification based on single-layer SAE and SVM[J]. Expert Systems with Applications (S0957-4174), 2015, 42(7): 3368-3380.
[5] Esam Othman, Yakoub Bazi, Naif Alajlan, et al.Using convolutional features and a sparse autoencoder for land-use scene classification[J]. International Journal of Remote Sensing (S0143-1161), 2016: 2149-2167.
[6] Makhzani A, Frey B.Winner-Take-All Autoencoders[J]. Eprint Arxiv(S1049-5258), 2014: 2773-2781.
[7] H Lee, R Grosse, R Ranganath, et al.Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations-[C]// Proceedings of the 26th Annual International Conference on Machine Learning. Canada, 2009: 609-616.
[8] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, et al.Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research (S1532-4435), 2010: 3371-3408.
[9] Du Bo, Xiong Wei, Wu Jia, et al.Stacked Convolutional Denoising AutoEncoders for Feature Representation[J]. IEEE Transactions on Cybernetics(S2168-2267), 2016: 1-11.
[10] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, et al.Extracting and composing robust features with denoising autoencoders[C]// Proceedin-ings of the 25th International Conference on Machine Learning, 2008: 1096-1103.
[11] Chow R, Zhong W, Blackmon M, et al.An efficient SVM-GA feature selection model for large healthcare databases[C]// Genetic and Evolutionary Computation Conference. USA, 2008: 1373-1380.
[12] Rifai S, Vincent P, Muller X, et al.Contractive Auto-Encoders: Explicit Invariance During Feature Extraction[C]// Proceedings of the 28th International Conference on Machine Learning. 2011: 833-840.
[13] Ju Y, Guo J, Liu S.A Deep Learning Method Combined Sparse Autoencoder with SVM[C]// International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. IEEE, 2015: 257-260.
[14] Bengio Y, Lamblin P, Popovici D, et al.Greedy layer-wise training of deep networks[C]// Proceedings of the 19th International Conference on Neural Information Processing Systems. Canada, 2007: 153-160.
[15] Dai C.SVM Visual Classification Based on Weighted Feature of Genetic Algorithm[C]// Sixth International Conference on Intelligent Systems Design and Engineering Applications. IEEE Computer Society, 2015: 786-789.
[16] Huang C L, Wang C G.A GA-based feature selection and parameters optimization for support vector machines[J]. Expert Systems with Applications (S0957-4174), 2006: 231-240.

基于稀疏自动编码器和支持向量机的图像分类

Image Classification Based on Sparse Autoencoder and Support Vector Machine

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