A study on general visual categorization of objects into animal and plant groups using global shape descriptors with a focus on category-specific deficits

TL;DR

This paper investigates how global shape descriptors can distinguish between animal and plant categories visually, especially in cases of semantic deficits, using machine learning methods to confirm category separability.

Contribution

It introduces a shape descriptor-based approach with feature learning to differentiate between animal and plant categories without relying on textural information.

Findings

Shape descriptors effectively distinguish animal and plant categories.

Supervised and unsupervised learning confirm the method's effectiveness.

General categories are visually distinguishable without texture details.

Abstract

How do humans distinguish between general categories of objects? In a number of semantic category deficits, patients are good at making broad categorization but are unable to remember fine and specific details. It has been well accepted that general information about concepts is more robust to damages related to semantic memory. Results from patients with semantic memory disorders demonstrate the loss of ability in subcategory recognition. In this paper, we review the behavioral evidence for category specific disorder and show that general categories of animal and plant are visually distinguishable without processing textural information. To this aim, we utilize shape descriptors with an additional phase of feature learning. The results are evaluated with both supervised and unsupervised learning mechanisms and confirm that the proposed method can effectively discriminates between animal and plant object categories in visual domain.