Assessing and optimising metadata quality for AI-generated images: A comparative analysis of generative AI and image recognition approaches

Abstract

The quality of metadata is critical for the effective organisation, retrieval and management of increasingly large repositories of AI-generated images. However, the absence of inherent contextual information in such images presents significant challenges in ensuring accurate and relevant metadata. This study evaluates the effectiveness of generative AI and image recognition approaches in improving metadata quality, based on 10 comprehensive criteria: semantic accuracy and relevance; thematic consistency and contextual adaptability; user-centric language and accessibility; consistency across data sets; emotional and aesthetic alignment; inclusivity and bias control; error detection and quality control; retrievability and search optimization; interpretive depth and granularity; and statistical performance measures. Results reveal that generative AI is particularly effective in generating contextually rich, adaptive keywords, while image recognition demonstrates superior object identification precision. Based on these insights, we propose a hybrid approach that integrates the contextual strengths of generative AI with the accuracy of image recognition. This hybrid method achieves significant improvements across all evaluations, including higher precision, recall, F1-score, cosine similarity and Jaccard index. By optimising metadata quality, the proposed approach facilitates more accurate and accessible retrieval of AI-generated images, significantly enhancing their usability and integration across diverse digital platforms.