Study of magnitude and extended relative phase information for color texture retrieval in L*a*b* color space

摘要

This paper presents a univariate multi-model for color texture characterization in luminance-chrominance (LC) color spaces. Such color spaces are known to separate between luminance and chrominance, contrary to RGB representation. The magnitude of the luminance component is then represented via the Gamma distribution. For chrominance, a new proposed information is investigated, namely the extended relative phase (ERP). This latter allows to capture the phase information inter-chrominance components. This information is characterized using the circular Wrapped cauchy (WC) distribution. The resulting {Gamma, WC} model has the advantage to be a linear-circular model. It, thus, represents color components considering their nature. Secondly, it permits to catch an information of dependence between chrominance components while being univariate. This means a modeling with simple feature extraction and straightforward implementation of similarity measurement, compared to the multivariate characterization widely used for color textures. Results on the Vistex database show that the study of {magnitude, ERP} information in LC color spaces, improves the retrieval performances compared to the RGB based characterization.