Computational models of machine vision goal, role and success

This paper surveys the learning algorithms of visual features representation and the computational modelling approaches proposed with the aim of developing better artificial object recognition systems. It turns out that most of the learning theories and schemas have been developed either in the spirit of understanding biological facts of vision or designing machines that provide better or competitive perception power than humans. In this study, we discuss and analyse the impact of notable statistical approaches that map the cognitive neural activity at macro level formally, as well as those that work independently without any biological inspiration towards the goal of developing better classifiers. With the ultimate objective of classification in hand, the dimensions of research in computer vision and AI in general, have expanded so much so that it has become important to understand if our goals and diagnostics of the visual input learning are correct or not. We first highlight the mainstream approaches that have been proposed to solve the classification task ever since the advent of the field, and then suggest some criterion of success that can guide the direction of the future research.