The origin and function of color in animals has been a subject of great interest for taxonomists and ecologists in recent years. Coloration in animals is useful for many important functions like species identification, camouflage and understanding evolutionary relationships. Quantitative measurements of color signal and patch size in mammals, birds and reptiles, to name a few are strong indicators of sexual selection cues and individual health. These measurements provide valuable insights into the impact of environmental conditions on habitat and breeding of mammals, birds and reptiles. Recent advances in the area of digital cameras and sensors have led to a significant increase in the use of digital photography as a means of color quantification in animals. Although a significant amount of research has been conducted on ways to standardize image acquisition conditions and calibrate cameras for use in animal color quantification, almost no work has been done on designing automated methods for animal color quantification. This thesis presents a novel perceptual"–"based framework for the automated extraction and quantification of animal coloration from digital images with slowly varying (almost homogenous) background colors. This implemented framework uses a combination of several techniques including color space quantization using a few dominant colors, foreground"–"background identification, Bayesian classification and mixture Gaussian modelling of conditional densities, edge"–"enhanced model"–"based classification and Saturation"–"Brightness quantization to extract the colored patch. This approach assumes no prior information about the color of either the subject or the background and also the position of the subject in the image. The performance of the proposed method is evaluated for the plumage color of the wild house finches. Segmentation results obtained using the implemented framework are compared with manually scored results to illustrate the performance of this system. The segmentation results show a high correlation with manually scored images. This novel framework also eliminates common problems in manual scoring of digital images such as low repeatability and inter"–"observer error.