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.

翻译：计算机视觉中的语义鸿沟问题源于视觉语义与词汇语义之间的错位，导致计算机视觉数据集设计和基准测试存在缺陷。本文提出，S.R. 阮冈纳赞的分类原则可为解决语义鸿沟问题及设计高质量计算机视觉数据集提供原则性起点。我们阐释了这些原则经过适当调整后如何支撑 vTelos 计算机视觉标注方法。本文还简要展示了实验证据，表明该方法能改进计算机视觉标注与准确性，从而验证 vTelos 的有效性。