Global Semantic Description of Objects based on Prototype Theory
This work addresses the need for interpretable semantic object descriptions in computer vision, though it appears incremental as it builds on existing CNN-classification models.
The paper tackles the problem of creating semantic object descriptions by proposing a Computational Prototype Model (CPM) that encodes central semantic meanings, resulting in a descriptor that significantly outperforms other image encodings in clustering and classification tasks.
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.