Is there a Best Color Space for Color Image Characterization or Representation based on Multivariate Gaussian Mixture Model ?
Résumé
The aim of this work is the evaluation of Multivariate Gaussian Mixture Model (MGMM) in different color spaces (RGB, L*a*b* and YC1C2 issued from IHLS). We have analysed the approach for the description of the color information in an image in the one hand and for the representation of a color image in the other hand. To achieve this goal we need an accurate estimation method of MGMM parameter set firstly and distances for evaluating MGMMs secondly. A SEMmean-EM procedure and two distances between color images and synthesized images are then proposed. First distance is based on normalized histograms and second distance is based on a psychovisual distance between two colors. To compute these distances, two methods for synthezising images are proposed from a direct procedure of site classification: for each site, sample a color from its associated Multivariate Gaussian law or attribute the mean color. The conclusion of our study is that estimation accuracy is better using L*a*b* color space than using RGB or YC1C2 and discrimination performances based on the two distances are also better in L*a*b* color space. A comparison between different criteria for choosing the number of components in the mixture is also done. Integrated Completed Likelihood criterion may be used if one only needs to characterize color information and Bayesian Information Criterion or phi(beta_min) criterion may be used if one needs to characterize color information along with color spatial localization.