Beyond Metadata: Next-Generation Video Archive Retrieval with a CLIP-Based Approach in NHK Archives

Large-scale video archives, such as the NHK Archives, face significant content retrieval challenges due to costly, labor-intensive, and often incomplete manual metadata. While AI offers solutions, cloud-based services introduce critical data security risks for sensitive assets. This report proposes a novel, metadata-free AI search methodology that prioritizes data privacy, cost-effectiveness, and operational efficiency. A core innovation is the pseudo-retrieval of video segments by targeting representative keyframes-potentially numbering in the billions-rather than processing entire video streams. This drastically reduces computational load. We employ an offline-capable, Japanese-language Contrastive Language-Image Pre-training (CLIP) model to extract rich semantic feature vectors from these keyframes. This model was deliberately chosen to ensure data security by eliminating cloud dependency and to provide an excellent balance of semantic understanding and computational efficiency for on-premise deployment. This approach enables intuitive multimodal search where users can query with natural language or reference images. An Approximate Nearest Neighbor (ANN) search index is implemented for rapid retrieval from the vast collection of feature vectors. The system features a hybrid architecture, combining secure on-premise processing with a scalable cloud-based search service to handle continuous content ingest. By simplifying video search to a highly efficient, keyframe-based semantic approach with a security-conscious AI model, our system significantly reduces the costs and effort of manual metadata generation. This methodology substantially enhances the accessibility, usability, and secure management of large-scale video archives, unlocking the full potential of these valuable resources.

Published

2025-10-13

Content type

Original Research

Keywords

nhk archives, content retrieval, keyframe-centric processing, clip (contrastive language-image pre-training), faiss (facebook ai similarity search), data security and privacy

ISBN

978-1-61482-966-9