A study on general visual categorization of objects into animal and plant groups using global shape descriptors with a focus on category-specific deficits
This work addresses visual categorization for understanding semantic memory deficits in patients, but it is incremental as it builds on existing shape descriptor methods.
The study tackled the problem of distinguishing between general object categories (animal vs. plant) in visual perception by using global shape descriptors with feature learning, and found that the proposed method effectively discriminates these categories without relying on textural information.
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.