Encoding and Decoding Narratives: Datafication and Alternative Access Models for Audiovisual Archives

TL;DR

This paper introduces a new ontological data model and retrieval workflow to improve access and interaction with large audiovisual archives, addressing complex metadata and hybrid queries.

Contribution

It presents an innovative ontological data model and a classifier-enhanced retrieval workflow for audiovisual archives, enhancing accessibility and user interaction.

Findings

Proposed an ontological data model for complex descriptors

Developed a classifier-enhanced retrieval workflow

Improved performance with data augmentation techniques

Abstract

Situated in the intersection of audiovisual archives, computational methods, and immersive interactions, this work probes the increasingly important accessibility issues from a two-fold approach. Firstly, the work proposes an ontological data model to handle complex descriptors (metadata, feature vectors, etc.) with regard to user interactions. Secondly, this work examines text-to-video retrieval from an implementation perspective by proposing a classifier-enhanced workflow to deal with complex and hybrid queries and a training data augmentation workflow to improve performance. This work serves as the foundation for experimenting with novel public-facing access models to large audiovisual archives