Generic multivariate model for color texture classification in RGB color space View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-09

AUTHORS

Ahmed Drissi El Maliani, Mohammed El Hassouni, Yannick Berthoumieu, Driss Aboutajdine

ABSTRACT

This paper presents a new method for modeling magnitudes of dual-tree complex wavelet coefficients, in the context of color texture classification. Based on the characterization of dependency between RGB color components, Gaussian copula associated with Generalized Gamma marginal function is proposed to design the multivariate generalized Gamma density (MGΓD) modeling. MGΓD has the advantages of genericity in terms of fitting over a variety of existing joint models. On the one hand, the generalized Gamma density function offers free-shape parameters to characterize a wide range of heavy-tailed densities, i.e., the genericity. On the other hand, the inter-component, inter-band dependency is captured by the Gaussian Copula which offers adapted flexibility. Moreover, this model leads to a closed form for the probabilistic similarity measure in terms of parameters, i.e., Kullback–Leibler divergence. By exploiting the separability between the copula and the marginal spaces, the closed form enables us to minimize the computational time needed to measure the discrepancy between two Multivariate Generalized Gamma densities in comparison to other models which imply using a Monte Carlo method characterized by an expensive time computing. For evaluating the performance of our proposal, a K-nearest neighbor (KNN) classifier is then used to test the classification accuracy. Experiments on different benchmarks using color texture databases are conducted to highlight the effectiveness of the proposed model associated to the Kullback–Leibler divergence. More... »

PAGES

217-231

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13735-014-0071-y

DOI

http://dx.doi.org/10.1007/s13735-014-0071-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035240129


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142 schema:name IPB, IMS, Groupe Signal, UMR 5218, Univ. Bordeaux, 33400, Talence, France
143 rdf:type schema:Organization
 




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