What can Computer Vision learn from Ranganathan?

TL;DR

This paper explores how Ranganathan's classification principles can address the Semantic Gap Problem in Computer Vision, leading to improved dataset design and annotation accuracy.

Contribution

It introduces the vTelos annotation methodology based on Ranganathan's principles to enhance CV dataset quality and reduce semantic misalignment.

Findings

Improved CV annotation accuracy with vTelos methodology

Demonstrated benefits of Ranganathan's principles in dataset design

Validated approach through experimental evidence

Abstract

The Semantic Gap Problem (SGP) in Computer Vision (CV) arises from the misalignment between visual and lexical semantics leading to flawed CV dataset design and CV benchmarks. This paper proposes that classification principles of S.R. Ranganathan can offer a principled starting point to address SGP and design high-quality CV datasets. We elucidate how these principles, suitably adapted, underpin the vTelos CV annotation methodology. The paper also briefly presents experimental evidence showing improvements in CV annotation and accuracy, thereby, validating vTelos.