Texture classification based on BIMF monogenic signals

Authors:
JianJia Pan;Yuan Yan Tang
Affiliations:
Department of Computer Science, Hong Kong Baptist University, Hong Kong;Department of Computer Science, Hong Kong Baptist University, Hong Kong,Department of Computer and Information Science, University of Macau, Macau, China
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Year:
2012

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Abstract

This paper proposes a new texture feature based on HHT, Riesz transform and LBP. Hilbert-Huang transform (HHT) is a novel efficient signal analysis method proposed by N.E.Huang. It consists two parts: Empirical Mode Decomposition (EMD) and Hilbert transform. Images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present new multi-scale characters and present illumination invariant. And then, for two-dimensional signal BIMFs, we proposed using the Riesz transform instead of Hilbert transform to generate monogenic signals, which are rotation invariant. After then, Local Binary Pattern (LBP) detected the features from the Monogenic-BIMFs space. Experiments demonstrate the LBP histogram of Monogenic-BIMFs present a better classification result than other state-of-the-art texture representation methods.