Modelling the Human Intuition to Complete the Missing Information in Images for Convolutional Neural Networks

TL;DR

This paper introduces a novel approach to enhance CNN performance by modeling human visual intuition based on Gestalt theory, enabling better completion of missing image information especially under occlusion.

Contribution

It proposes a formal model of visual intuition inspired by Gestalt theory and integrates it into CNNs to improve accuracy on incomplete images.

Findings

Augmented CNNs outperform classic models on incomplete images

Model effectively completes missing visual information

Performance gains observed with MNIST dataset

Abstract

In this study, we attempt to model intuition and incorporate this formalism to improve the performance of the Convolutional Neural Networks. Despite decades of research, ambiguities persist on principles of intuition. Experimental psychology reveals many types of intuition, which depend on state of the human mind. We focus on visual intuition, useful for completing missing information during visual cognitive tasks. First, we set up a scenario to gradually decrease the amount of visual information in the images of a dataset to examine its impact on CNN accuracy. Then, we represent a model for visual intuition using Gestalt theory. The theory claims that humans derive a set of templates according to their subconscious experiences. When the brain decides that there is missing information in a scene, such as occlusion, it instantaneously completes the information by replacing the missing parts with the most similar ones. Based upon Gestalt theory, we model the visual intuition, in two layers. Details of these layers are provided throughout the paper. We use the MNIST data set to test the suggested intuition model for completing the missing information. Experiments show that the augmented CNN architecture provides higher performances compared to the classic models when using incomplete images.