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Study of the relative magnitude in the wavelet domain for texture characterization

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Abstract

Wavelet-based transforms have emerged as efficient directional multiscale schemes able to provide advanced analysis for the textural content of an image. Making use of their statistical dependencies, wavelet coefficients have been recognized as good basis for texture analysis. In this paper, we propose a new feature vector called relative magnitude (RM) which incorporates local statistical dependencies within the neighborhood of magnitude coefficients. Its discriminative power is evaluated on multiclass grayscale texture classification. The generalized Gaussian distribution and the Laplace Model are used to study the statistical behavior of the proposed feature vector. Experiments were conducted on textures from the VisTex, Brodatz, Outex_TC10, UMD, UIUC, and KTH_TIPS databases. Quantitative results demonstrate the efficiency of the RM feature vector for texture discrimination in the wavelet domain.

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Authors and Affiliations

  1. LRIT Laboratory, CNRST-URAC o̱29 Unit, Faculty of Sciences, Mohammed V University in Rabat, B.P. 1014, Rabat, Morocco

    Hind Oulhaj, Mohammed El Hassouni & Mohammed Rziza

  2. I3MTO Laboratory, Univ. Orléans, EA 4708, 5, rue de Chartres, 45067, Orléans, France

    Rachid Jennane

  3. ENSA, Ibn Tofail University, B.P. 242, Kenitra, Morocco

    Aouatif Amine

  4. DESTEC-FLSHR, Mohammed V University in Rabat, B.P. 1014, Rabat, Morocco

    Mohammed El Hassouni

Authors
  1. Hind Oulhaj
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  2. Rachid Jennane
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  3. Aouatif Amine
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  4. Mohammed El Hassouni
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  5. Mohammed Rziza
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Correspondence to Hind Oulhaj.

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Oulhaj, H., Jennane, R., Amine, A. et al. Study of the relative magnitude in the wavelet domain for texture characterization. SIViP 12, 1403–1410 (2018). https://doi.org/10.1007/s11760-018-1295-8

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  • DOI: https://doi.org/10.1007/s11760-018-1295-8

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