Semantic search, powered by large multi-modal models like CLIP and Amazon OpenSearch Service, enhances image retrieval accuracy by addressing the limitations of traditional tag-based approaches. By embedding images and search queries into vectors within the same semantic space, this method allows for a more intuitive and accurate search experience. This process involves image embedding, vector indexing, query embedding, and nearest neighbor search, resulting in more relevant search results.
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IntroductionProblem: Tag-based approaches to image search have many limitationsSolution overviewImplementing semantic image search with Amazon OpenSearch Service and CLIPSolution resultSummarySort: