The Neural Correlates of Image Texture in the Human Vision Using Magnetoencephalography

TL;DR

This study explores how the human brain processes image textures by analyzing MEG data and shows a hierarchical pattern in neural responses to different texture features, with energy having a sustained correlation.

Contribution

It introduces a novel approach combining MVPA and SVM to trace neural signatures of texture features in the human visual system using MEG data.

Findings

Hierarchical processing order: contrast, homogeneity, energy, correlation.

Energy feature shows sustained correlation with brain activity.

Neural signatures of texture features can be traced over time in MEG data.

Abstract

Undoubtedly, textural property of an image is one of the most important features in object recognition task in both human and computer vision applications. Here, we investigated the neural signatures of four well-known statistical texture features including contrast, homogeneity, energy, and correlation computed from the gray level co-occurrence matrix (GLCM) of the images viewed by the participants in the process of magnetoencephalography (MEG) data collection. To trace these features in the human visual system, we used multivariate pattern analysis (MVPA) and trained a linear support vector machine (SVM) classifier on every timepoint of MEG data representing the brain activity and compared it with the textural descriptors of images using the Spearman correlation. The result of this study demonstrates that hierarchical structure in the processing of these four texture descriptors in the human brain with the order of contrast, homogeneity, energy, and correlation. Additionally, we found that energy, which carries broad texture property of the images, shows a more sustained statistically meaningful correlation with the brain activity in the course of time.