Modeling Visual Information Processing in Brain: A Computer Vision Point of View and Approach

TL;DR

This paper proposes a unifying framework for visual information processing based on Kolmogorov complexity and algorithmic information, aiming to better model human-like image processing in computer vision.

Contribution

It introduces a new definition of visual information and a framework that explicitly incorporates perceptual and cognitive aspects of image processing.

Findings

Framework aligns with biological vision features

Potential to improve human-like image processing models

Addresses fundamental issues in visual information theory

Abstract

We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about "What is information?" In particular, what is "visual information" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of "information", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.