Representation interest point using empirical mode decomposition and independent components analysis

  • Authors:

  • Dongfeng Han;Wenhui Li;Xiaosuo Lu;Yi Wang;Ming Li

  • Affiliations:

  • College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, P.R. China;College of Computer Science and Technology, Key Laboratory of Symbol, Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, P.R. China;JiLin Province Economic Information Center, China

  • Venue:
  • ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
  • Year:
  • 2006

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Abstract

This paper presents a new interest point descriptors representation method based on empirical mode decomposition (EMD) and independent components analysis (ICA). The proposed algorithm first finds the characteristic scale and the location of the interest points using Harris-Laplacian interest point detector. We then apply the Hilbert transform to each component and get the amplitude and the instantaneous frequency as the feature vectors. Then independent components analysis is used to model the image subspace and reduces the dimension of the feature vectors. The aim of this algorithm is to find a meaningful image subspace and more compact descriptors. Combination the proposed descriptors with an effective interest point detector, the proposed algorithm has a more accurate matching rate besides the robustness towards image deformations.