Biologically inspired feature-based categorization of objects

Abstract

We have developed a method for clustering features into objects by taking those features which include intensity, orientations and colors from the most salient points in an image as determined by our biologically motivated saliency program. We can train a program to cluster these features by only supplying as training input the number of objects that should appear in an image. We do this by clustering from a technique that involves linking nodes in a minimum spanning tree by not only distance, but by a density metric as well. We can then form classes over objects or object segmentation in a novel validation set by training over a set of seven soft and hard parameters. We discus as well the uses of such a flexible method in landmark based navigation since a robot using such a method may have a better ability to generalize over the features and objects.