Spatio-Chromatic Opponent Features

Abstract

This work proposes colour opponent features that are based on low-level models of mammalian colour visual processing. A key step is the construction of opponent spatio-chromatic feature maps by filtering colour planes with Gaussians of unequal spreads. Weighted combination of these planes yields a spatial center-surround effect across chromatic channels. The resulting feature spaces – substantially different to CIELAB and other colour-opponent spaces obtained by colour-plane differencing – are further processed to assign local spatial orientations. The nature of the initial spatio-chromatic processing requires a customised approach to generating gradient-like fields, which is also described. The resulting direction-encoding responses are then pooled to form compact descriptors. The individual performance of the new descriptors was found to be substantially higher than those arising from spatial processing of standard opponent colour spaces, and these are the first chromatic descriptors that appear to achieve such performance levels individually. For all stages, parametrisations are suggested that allow successful optimisation using categorization performance as an objective. Classification benchmarks on Pascal VOC 2007 and Bird-200-2011 are presented to show the merits of these new features.