Texture Classification Through Directional Empirical Mode Decomposition

Authors:
Zhongxuan Liu;Hongjian Wang;Silong Peng
Affiliations:
Chinese Academy of Sciences, China;Chinese Academy of Sciences, China;Chinese Academy of Sciences, China
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Year:
2004

Citing 0
Cited 6

Technical Section: Texture synthesis based on Direction Empirical Mode Decomposition

Computers and Graphics
Feature point detection utilizing the empirical mode decomposition

EURASIP Journal on Advances in Signal Processing
Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation

EURASIP Journal on Advances in Signal Processing
Dim target detection and tracking based on empirical mode decomposition

Image Communication
Improved bi-dimensional EMD and Hilbert spectrum for the analysis of textures

Pattern Recognition
On analysis of bi-dimensional component decomposition via BEMD

Pattern Recognition

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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 technique.