Multiscale color invariants based on the human visual system
This paper proposes a new representation for color texture using a set of multiscale illuminant invariant features. The approach was specifically developed to investigate the feasibility of using machine vision to automatically monitor populations of animal species in ecologically sensitive regions, such as the Amazon Forest. The approach uses a combination of Finlayson's color angle idea and Gabor multichannel filters and was inspired by the multichannel model of the Human Visual System (HVS). Using a database of color textures from three species of Amazonian monkey, and also a previously published reference database of color regions, we show that the approach performs better than methods based on color angles or Gabor filters alone. The Monkey database was compiled from texture segments extracted from a video of the Amazon Forest using a spatio-temporal segmentation algorithm. The approach is evaluated by applying two different classification tests in order to measure the quality of the recognition features root mean square (rms) analysis and receiver operating characteristic (ROC) analysis.
human visual system illuminant invariants gabor filters color texture images image classification and recognition roc analysis