Communication Dans Un Congrès Année : 2019

Compact Color Texture Representation by Feature Selection in Multiple Color Spaces

Résumé

This paper presents a compact color texture representation based on the selection of features extracted from different configurations of descriptors computed in multiple color spaces. The proposed representation aims to take simultaneously into account several spatial and color properties of different textures. For this purpose, texture images are coded in five different color spaces. Then, texture descriptors with different neighborhood and quantization parameter settings, are calculated from this images in order to extract a high dimensionality feature vector describing the textures. Compact representation is finally obtained by means of a feature selection scheme. Our approach is applied with two well-known color texture descriptors for the classification of three benchmark image databases.

Fichier principal
Vignette du fichier
Version finale publiable.pdf (174.73 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
Licence

Dates et versions

hal-03031984 , version 1 (16-02-2023)

Licence

Identifiants

Citer

M. Alimoussa, Nicolas Vandenbroucke, Alice Porebski, R. Thami, S. El Fkihi, et al.. Compact Color Texture Representation by Feature Selection in Multiple Color Spaces. VISAPP 2019 : 14th International Conference on Computer Vision Theory and Applications, Feb 2019, Prague, Czech Republic. pp.436-443, ⟨10.5220/0007578704360443⟩. ⟨hal-03031984⟩
123 Consultations
130 Téléchargements

Altmetric

Partager

  • More