Automated bird plumage coloration quantification in digital images

Abstract

Quantitative measurements of bird plumage color and patch size provide valuable insights into the impact of environmental conditions on the habitat and breeding of birds. This paper presents a novel perceptual-based framework for the automated extraction and quantification of bird plumage coloration from digital images with slowly varying background colors. The image is first coarsely segmented into a few classes using the dominant colors of the image in a perceptually uniform color space. The required foreground class is then identified by eliminating the dominant background color based on the color histogram of the image. The determined foreground is segmented further using a Bayesian classifier and an edge-enhanced model-based classification for eliminating regions of human skin and is refined by using a perceptual-based Saturation-Brightness quantization to only preserve the perceptually relevant colors. Results are presented to illustrate the performance of the proposed method.