Characterising Facial Gender Difference Using Fisher-Rao Metric

Ceolin, Simone Regina; Hancock, Edwin R.

doi:10.1109/icpr.2010.1047

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…L Margins (f (x; w (1) )||f (x; w (2) )) (9) where r i , i = 1, ..., d represents the eigenvalues of Σ −1 1 Σ 2 . Expressions of L Margins for Weibull and Gamma marginals were provided in [5] [16], for the vonMises marginal expression of the Rao distance can be found in [15].…”

Section: Similarity Measurement Based On Rao Distancementioning

confidence: 99%

Multi-model Approach for Multicomponent Texture Classification

Maliani

Berthoumieu

Aboutajdine

2012

Lecture Notes in Computer Science

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This paper concerns multicomponent texture classification. The aim is to provide a flexible model when wavelet subband coefficients of components do not have the same distributions. Example of such case is when color textures are represented in a perceptual color space. In this kind of representation, the separability between luminance and chrominance components have to be considered in the modeling process. The contribution of this work consists in proposing a multi-model based characterization for this type of multicomponent images. For this, two models ML and MC r are used in order to extract features from luminance and chrominance components, respectively. We discuss in detail and define the multi-model when textures are represented in the HSV color space as a special case of multicomponent analysis. Experimental results show that the proposed approach improves performances of the classification system when compared with existing methods.

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Section: Similarity Measurement Based On Rao Distancementioning

confidence: 99%

Multi-model Approach for Multicomponent Texture Classification

Maliani

Berthoumieu

Aboutajdine

2012

Lecture Notes in Computer Science

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Color texture classification method based on a statistical multi-model and geodesic distance

Maliani¹,

Berthoumieu²,

Aboutajdine³

2014

Journal of Visual Communication and Image Representation

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International audienceIn this letter, we propose a novel color texture classification method based on statistical characterization. The approach consists in modeling complex wavelet coefficients of both luminance and chrominance components separately leading to a multi-modeling approach. The copula theory allows to take into account the spatial dependencies which exist within the intra-luminance sub-bands via the luminance model M L , and also between the inter-chrominance subband coefficients via the chrominance model M Cr. The multi-model, i.e M L and M Cr , is used to develop a Bayesian classifier based on the softmax principal. To derive the classifier, we propose a closed-form expression for the Rao geodesic distance between two copulas. Experiments on two sub-families of luminance-chrominance color spaces namely Lab and HSV have been carried out for a wide range of color texture databases. The combination of different statistical sub-models show that the multi-modeling performs better than some existing methods in term of classification rates

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Characterising Facial Gender Difference Using Fisher-Rao Metric

Cited by 2 publications

References 11 publications

Multi-model Approach for Multicomponent Texture Classification

Multi-model Approach for Multicomponent Texture Classification

Color texture classification method based on a statistical multi-model and geodesic distance

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