Non Linear Cellular Automata Enhanced with Active Learning for Pattern Classification in Highly Dense Images

Abstract

This paper introduces a new approach to classify several high density images based on the properties of Non Linear Cellular Automata. We use a state-transition which consists of a set of disjoint trees rooted at cyclic states of unit cycle length thus forming a natural classifier. The framework proposed is strengthened with genetic algorithm to find the desired local rule of the modeling as a global state function.