Object Class Recognition Using NEAT-Evolved Artificial Neural Network
Mozaherul Hoque Abul Hasanat, Siti Zubaidah Harun, Dhanesh Ramachandram, Mandava Rajeswari · 2008
Object class recognition is a highly challenging area in computer vision and machine learning. In this paper, we introduce a novel approach to object class recognition using Neuro Evolution of Augmenting Topologies (NEAT) to evolve artificial neural networks (ANN) capable of taking advantage of the robust SIFT feature based descriptor histograms. We claim that NEAT can produce ANN classifier which exhibits outstanding ability of learning from only afew training examples without sacrificing accuracy. Our empirical evaluations against the performance of state of the art statistical machine learning method such as support vector machine show that NEAT-evolved ANN classifier outperforms by an average of 9.96% higher accuracy when presented with very small training set proving its superior ability to generalize its learning.