CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Texture classification through directional empirical mode decomposition
Liu Zhongxuan; Wang Hongjian; Peng Silong; Zhongxuan Liu
2004
Conference NameProceedings of the 17th International Conference on Pattern Recognition ICPR 2004
Pagespp 803-806
Conference Date2004/8/23-2004/8/26
Conference PlaceUnited kingdomCambridgeUnitedkingdom
AbstractThis 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.
Keyword
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12899
Collection智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorZhongxuan Liu
Recommended Citation
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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