Texture classification through directional empirical mode decomposition
Liu Zhongxuan; Wang Hongjian; Peng Silong; Zhongxuan Liu
2004
会议名称Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004
页码pp 803-806
会议日期2004/8/23-2004/8/26
会议地点United kingdomCambridgeUnitedkingdom
摘要This paper presents a method for texture classification through Directional Empirical Mode Decomposition (DEMD). Although there have been many filtering based techniques proposed for texture retrieval problems of non-adaptivity and redundancy are still hard to solve simultaneously. As a technique being introduced into signal processing recently Empirical Mode Decomposition (EMD) is an adaptive and approximately orthogonal filtering process. To apply EMD to texture classification we propose a new method of extending 1-D EMD to 2-D case called DEMD. The approach adaptively decomposes images into local narrow band ingredients-Intrinsic Mode Functions (IMFs) and extracts their features including frequency and envelopes. To improve its classification ability the fractal dimensions of the IMFs are also considered. Decomposition of several directions is computed for rotation invariance. Experiments for textures in Brodatz set and USC database indicate the effectiveness of our technique.
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文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12899
专题智能制造技术与系统研究中心_多维数据分析
通讯作者Zhongxuan Liu
推荐引用方式
GB/T 7714
Liu Zhongxuan,Wang Hongjian,Peng Silong,et al. Texture classification through directional empirical mode decomposition[C],2004:pp 803-806.
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