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Abstract
This paper presents a new wavelet based retrieval approach based on Spherically Invariant Random Vector (SIRV) modeling of wavelet subbands. Under this multivariate model, wavelet coefficients are considered as a realization of a random vector which is a product of the square root of a scalar random variable (called multiplier) with an independent Gaussian vector. We propose to work on the joint distribution of the scalar multiplier and the multivariate Gaussian process. For measuring similarity between two texture images, the geodesic distance is provided for various multiplier priors. A comparative study between the proposed method and conventional models on the VisTex image database is conducted and indicates that SIRV modeling combined with geodesic distance achieves higher recognition rates than classical approaches.
Keywords
Multiscale analysis, Texture, Kullback-Leibler divergence, Geodesic distance, Spherically Invariant Random Vector

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Please use this url to cite or link to this publication:

Chicago
Bombrun, Lionel, Nour-Eddine Lasmar, Yannick Berthoumieu, and Geert Verdoolaege. 2011. “Multivariate Texture Retrieval Using the SIRV Representation and the Geodesic Distance.” In International Conference on Acoustics Speech and Signal Processing ICASSP, 865–868. New York, NY, USA: IEEE.
APA
Bombrun, L., Lasmar, N.-E., Berthoumieu, Y., & Verdoolaege, G. (2011). Multivariate texture retrieval using the SIRV representation and the geodesic distance. International Conference on Acoustics Speech and Signal Processing ICASSP (pp. 865–868). Presented at the IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP 2011), New York, NY, USA: IEEE.
Vancouver
1.
Bombrun L, Lasmar N-E, Berthoumieu Y, Verdoolaege G. Multivariate texture retrieval using the SIRV representation and the geodesic distance. International Conference on Acoustics Speech and Signal Processing ICASSP. New York, NY, USA: IEEE; 2011. p. 865–8.
MLA
Bombrun, Lionel, Nour-Eddine Lasmar, Yannick Berthoumieu, et al. “Multivariate Texture Retrieval Using the SIRV Representation and the Geodesic Distance.” International Conference on Acoustics Speech and Signal Processing ICASSP. New York, NY, USA: IEEE, 2011. 865–868. Print.
@inproceedings{2032811,
  abstract     = {This paper presents a new wavelet based retrieval approach based on Spherically Invariant Random Vector (SIRV) modeling of wavelet subbands. Under this multivariate model, wavelet coefficients are considered as a realization of a random vector which is a product of the square root of a scalar random variable (called multiplier) with an independent Gaussian vector. We propose to work on the joint distribution of the scalar multiplier and the multivariate Gaussian process. For measuring similarity between two texture images, the geodesic distance is provided for various multiplier priors. A comparative study between the proposed method and conventional models on the VisTex image database is conducted and indicates that SIRV modeling combined with geodesic distance achieves higher recognition rates than classical approaches.},
  author       = {Bombrun, Lionel and Lasmar, Nour-Eddine and Berthoumieu, Yannick and Verdoolaege, Geert},
  booktitle    = {International Conference on Acoustics Speech and Signal Processing ICASSP},
  isbn         = {9781457705397},
  issn         = {1520-6149},
  keyword      = {Multiscale analysis,Texture,Kullback-Leibler divergence,Geodesic distance,Spherically Invariant Random Vector},
  language     = {eng},
  location     = {Prague, Czech Republic},
  pages        = {865--868},
  publisher    = {IEEE},
  title        = {Multivariate texture retrieval using the SIRV representation and the geodesic distance},
  url          = {http://dx.doi.org/10.1109/ICASSP.2011.5946541},
  year         = {2011},
}

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