|Texture classification through directional empirical mode decomposition|
|Liu Zhongxuan; Wang Hongjian; Peng Silong; Zhongxuan Liu
|Conference Name||Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004
|Conference Place||United kingdomCambridgeUnitedkingdom
|Abstract||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
|Corresponding Author||Zhongxuan Liu|
Liu Zhongxuan,Wang Hongjian,Peng Silong,et al. Texture classification through directional empirical mode decomposition[C],2004:pp 803-806.
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