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Video search

As explained in AI-Powered Search

Video search retrieves video content or scenes matching textual, audio, or visual query descriptions.

Video search retrieves scenes by matching text descriptions, audio clips, or images against indexed video representations. Because video combines sequential images and audio, search can use either modality or both. section 15.3.1

A practical pipeline maps video frames, audio, and written text into overlapping vector spaces. It indexes every frame or samples a few frames per second, depending on the required search granularity, then compares queries with those representations. section 15.3.1

Multimodal scene retrieval

A text query can locate a scene matching its description; an audio clip can retrieve related video; an image can find the most similar video. Audio-aware search can also target sounds such as a loud train or rushing stream. section 15.3.1

How AI-Powered Search covers it

How deep it goes
Explains multimodal indexing and vector matching, but stops short of implementation code, ranking details, or evaluation procedures.
Examples
Uses scene descriptions, audio clips, images, loud trains, rushing streams, and accented speech as retrieval queries.
What you can do after
Evaluate/understand how to design a video search engine around sampled frames and shared multimodal representations.
How it connects
Connects computer vision, natural language processing, audio processing, speech recognition, language models, and vector spaces.

This explainer was generated by Manning's knowledge graph from the text of AI-Powered Search. The section links point to the parts of the book it draws on.