How AI Powers Next‑Gen Multimedia Content Retrieval: From OCR to Knowledge Graphs

Evolution of Search and the Rise of Multimedia Retrieval

Search has progressed through four stages: (1) category navigation (e.g., early portals like 123.com and Yahoo), (2) text retrieval using keyword matching and models such as BM25, (3) integrated analysis driven by hyperlink analysis (Google, Baidu), and (4) user‑scenario‑centric search where mobile apps replace a single PC entry point, making multimedia retrieval essential.

What Is Multimedia Content Retrieval?

Multimedia content retrieval involves two modalities: the indexed media (which now includes not only text but also visual, audio, and facial data) and the query media (which can be text, images, audio, or video). Effective retrieval requires deep content understanding on both sides.

User Scenarios

Typical multimodal queries include:

Voice commands (e.g., "I want to watch X movie"), processed by ASR → NLU → semantic search.

Image queries (e.g., taking a photo of a foreign food while traveling and searching for similar items).

Video‑based searches using facial recognition to find a celebrity or a similar person.

Traditional keyword queries, which remain the dominant use case.

Key Technical Challenges

Retrieving video content based solely on titles fails when relevant information appears only within video frames, such as a historical speech that lacks keyword matches. The core challenge is transforming unstructured video data into structured, multi‑dimensional representations.

Core AI Techniques

1. OCR Technology

OCR, dating back to the 1970s, is adapted for video subtitles and on‑screen text. Specialized video OCR achieves about 70% character accuracy, but struggles with complex backgrounds and varying text positions.

2. Face Recognition

Faces are detected and aligned from video frames, then encoded into vectors for offline indexing. Online queries convert input images into vectors and perform nearest‑neighbor search to retrieve matching videos.

3. Content Knowledge Graph

Video content is parsed into entities (people, objects, topics) and relationships, forming a knowledge graph with triples (subject‑predicate‑object). Techniques include entity extraction, recognition, and linking. The graph enables structured queries such as "Find shows related to Beijing Women’s Documentary".

Semantic Relevance Modeling

The team employs a bidirectional LSTM with attention to compute semantic similarity between query and video content. Initial models focused on text; later they incorporated OCR‑derived text and other multimodal signals, converting sparse features into dense representations.

Ranking and Fusion Model

Beyond relevance, the system combines multiple models—timeliness, authority, and others—into a fused ranking model. A monolithic end‑to‑end model was rejected because it limited adjustability and could not address specific relevance issues.

Conclusion

The presentation outlines how AI techniques—OCR, face recognition, knowledge graphs, and semantic similarity models—transform raw video streams into searchable, structured data, enabling rich multimodal user experiences across e‑commerce, entertainment, and education.