现代电子技术Issue(6)：20-24,5.
基于堆栈降噪自动编码模型的动态纹理分类方法
Dynamic texture classification method based on stacked denoising autoencoding model
摘要
Abstract
To overcome the shortcomings of extracting the feature descriptors by manual operation and too high feature di⁃mension for dynamic scene classification,a deep learning network model is proposed to extract dynamic texture features. First⁃ly,the slow feature analysis method is used to learn dynamic characteristics of each video sequence through before hand,and the learned feature is used as input data of deep learning to get the advanced representation of the input signal. The stacked de⁃noising autoencoding model is selected for the deep learning network mode. SVM classification method is used for its classifica⁃tion. The experimental result proves that the feature dimension extracted by this method is low and can effectively describe dy⁃namic textures.关键词
动态纹理分类/慢特征分析/深度学习/堆栈降噪自动编码网络模型Key words
dynamic texture classification/slow feature analysis/deep learning/stacked denoising autoencoding model分类
信息技术与安全科学引用本文复制引用
汪彩霞,魏雪云,王彪..基于堆栈降噪自动编码模型的动态纹理分类方法[J].现代电子技术,2015,(6):20-24,5.基金项目
国家自然科学基金（11204109）；江苏省高校自然科学基金 （）
