An adaptive machine learning algorithm for color image analysis and processing

Abstract

In this paper, a new adaptive machine learning algorithm for analyzing and processing color images of natural scenes is presented. The eventual goal of this research is to obtain a mathematical training algorithm to guide the operation of an unsupervised pattern recognition and classification technique for detecting and extracting the image modes or clusters in a selected or constructed feature space. For this purpose, the peak modality of one-dimensional (1-D) image histograms is selected as the mathematical training criterion. Area, mode dispersion, approximated curvature and steepness are some of the measured quantities for a modality test. Linear discriminant function is then used to extract the detected image clusters in the feature or measurement space.