# Global Semantic Description of Objects based on Prototype Theory

## TL;DR

This paper presents a new semantic description method for objects based on Prototype Theory, using a Computational Prototype Model to encode category meaning and improve object classification and clustering.

## Contribution

It introduces a novel CPM and description model that leverage CNN-derived prototypes for interpretable, discriminative object descriptions, outperforming existing global encodings.

## Key findings

- CPM effectively models internal semantic structure of categories.
- Semantic distance correlates with object typicality.
- Descriptor encoding outperforms other global encodings in clustering/classification.

## Abstract

In this paper, we introduce a novel semantic description approach inspired on Prototype Theory foundations. We propose a Computational Prototype Model (CPM) that encodes and stores the central semantic meaning of objects category: the semantic prototype. Also, we introduce a Prototype-based Description Model that encodes the semantic meaning of an object while describing its features using our CPM model. Our description method uses semantic prototypes computed by CNN-classifications models to create discriminative signatures that describe an object highlighting its most distinctive features within the category. Our experiments show that: i) our CPM model (semantic prototype + distance metric) is able to describe the internal semantic structure of objects categories; ii) our semantic distance metric can be understood as the object visual typicality score within a category; iii) our descriptor encoding is semantically interpretable and significantly outperforms other image global encodings in clustering and classification tasks.