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Gaussian MRF Rotation-Invariant Features for Image Classification.
- Source :
-
IEEE Transactions on Pattern Analysis & Machine Intelligence . Jul2004, Vol. 26 Issue 7, p951-955. 5p. - Publication Year :
- 2004
-
Abstract
- Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate east squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 26
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 13484726
- Full Text :
- https://doi.org/10.1109/TPAMI.2004.30