Edge co-occurrences can account for rapid categorization of natural versus animal images

Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically at increasing levels of abstraction, from edge ex...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2015-06, Vol.5 (1), p.11400-11400, Article 11400
Main Authors: Perrinet, Laurent U., Bednar, James A.
Format: Article
Language:English
Subjects:
631/378/116/1925
631/378/2613/2616
631/378/2649/1723
639/705/531
Algorithms
Classification
Cognition
Data processing
Humanities and Social Sciences
Humans
Judgment
Life Sciences
multidisciplinary
Neural coding
Neurons and Cognition
Pattern recognition
Pattern Recognition, Visual
Photic Stimulation
Science
Semantics
Signal processing
Statistical analysis
Visual cortex
Visual Perception
Visual system
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically at increasing levels of abstraction, from edge extraction to mid-level object recognition and then object categorization. Here we show that the statistics of edge co-occurrences alone are sufficient to perform a rough yet robust (translation, scale and rotation invariant) scene categorization. We first extracted the edges from images using a scale-space analysis coupled with a sparse coding algorithm. We then computed the “association field” for different categories (natural, man-made, or containing an animal) by computing the statistics of edge co-occurrences. These differed strongly, with animal images having more curved configurations. We show that this geometry alone is sufficient for categorization and that the pattern of errors made by humans is consistent with this procedure. Because these statistics could be measured as early as the primary visual cortex, the results challenge widely held assumptions about the flow of computations in the visual system. The results also suggest new algorithms for image classification and signal processing that exploit correlations between low-level structure and the underlying semantic category.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep11400