Multiscale color and texture invariants for image recognition

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 the Amazon Forest. The approach uses a combination of Finlayson's (1996) color angle idea and Gabor multichannel filters. Using a database of color textures from 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 spatial-temporal segmentation algorithm. The approach is evaluated by applying two classification tests in order to measure the quality of the recognition features: root mean square (RMS) analysis and receiver operating characteristic (ROC) analysis.