Normalized Cut optimization based on color perception findings. A comparative study

Abstract

This paper proposes a methodology to obtain a fully automatic color segmentation algorithm based on the Normalized Cut (Ncut) proposed by Shi and Malik, using recent findings in color perception. A weighting matrix computed using a perceptually uniform color space (CIE $$L^*a^*b^*$$ L ? a ? b ? ) and color distance formulae correlated with the visually perceived color differences (CIE94 and CIEDE2000); a stopping condition related to perceptual criteria; an automatic parameters setting required to compute the affinity matrix are proposed. To test the proposed methodology, a wide study about the influence of the color space choice, different stopping conditions, and different similarity measurements is carried out. These alternatives are exhaustively evaluated using perception-related measurements (S-CIELAB) and general segmentation evaluation metrics applied to the 500 images of the Berkeley database. The results showed that the proposed method outperforms Ncut based on other color spaces, similarity measure or stopping conditions. Furthermore, the usability of the method is increased by replacing the manual parameter setting for an automatic.