What can Computer Vision learn from Ranganathan?
This addresses dataset design flaws in computer vision, but it is incremental as it adapts existing principles to a new domain.
The paper tackles the Semantic Gap Problem in Computer Vision by proposing Ranganathan's classification principles as a basis for designing better datasets, showing experimental improvements in annotation and accuracy.
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.