Embedding Similarity Scoring for Smarter Image Retrieval Systems

Re-ranking in information retrieval improves search accuracy by reordering initial results using methods like relevance feedback, learning-to-rank models, and contextual embeddings. This article explores ColBERT’s contextualized late interaction framework and extends it into a two-stage re-ranking method for image retrieval. By combining filtering, similarity scoring, and localization, the approach enhances not only the precision of retrieved results but also the ability to identify anatomical regions of interest in complex datasets.