Semantic Analysis of (Reflectional) Visual Symmetry: A Human-Centred Computational Model for Declarative Explainability
This work addresses the need for explainable AI in visual symmetry analysis, with applications in the arts and psychological sciences, though it appears incremental as it builds on existing methods.
The paper tackles the problem of semantically interpreting symmetry in naturalistic scenes by developing a computational model that integrates knowledge representation and deep learning, resulting in a framework capable of generating human-centred, queryable relational structures and evaluated through an empirical study on human perception.
We present a computational model for the semantic interpretation of symmetry in naturalistic scenes. Key features include a human-centred representation, and a declarative, explainable interpretation model supporting deep semantic question-answering founded on an integration of methods in knowledge representation and deep learning based computer vision. In the backdrop of the visual arts, we showcase the framework's capability to generate human-centred, queryable, relational structures, also evaluating the framework with an empirical study on the human perception of visual symmetry. Our framework represents and is driven by the application of foundational, integrated Vision and Knowledge Representation and Reasoning methods for applications in the arts, and the psychological and social sciences.