This whitepaper explores how machine learning enables visual similarity search by identifying visually related images rather than relying on text-based metadata. It explains how deep neural networks extract feature embeddings that represent visual content and allow similarity comparisons at scale. The paper discusses model architectures, indexing techniques, and distance metrics used to retrieve visually similar assets efficiently. Practical use cases include product discovery in eCommerce, digital asset management, visual inspection, and fraud detection. The whitepaper highlights how visual similarity search improves user experience, reduces manual tagging, and unlocks value from large, unstructured image collections.

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