What Is Semantic Video Search? A Plain-English Guide for Editors
Semantic search understands what you mean, not just what you type. Here is how it works for video, why it matters, and what it means for your editing workflow.
You type "interview about budget concerns" into a search bar. Traditional search looks for those exact words. Semantic search understands you want footage of someone talking about money worries, even if nobody in the video ever says the word "budget."
That is the core difference, and it changes everything about how you find footage.
Keyword search vs semantic search
Keyword search matches exact text. If you search for "sunset" you get results that contain the word "sunset" in their filename, tags, or transcript. If nobody tagged the clip and nobody said "sunset" on camera, the search returns nothing, even if the clip is a gorgeous golden-hour shot.
Semantic search matches meaning. It understands that "sunset," "golden hour," "dusk," and "sun going down" all describe similar things. More importantly, it can match visual content: a clip showing a sunset can be found by searching "sunset" even if the word appears nowhere in the metadata.
For video editors, this distinction is enormous. Your footage does not come pre-tagged with perfect keywords. Transcripts capture what people said, not what the camera saw. Traditional search only works if someone did the manual labour of describing every clip. Semantic search works on the actual content.
How semantic video search works
Semantic search for video is harder than for text, because video contains multiple types of information simultaneously. FrameQuery analyses each of these modalities and makes them all searchable.
Speech and transcription
The audio track gets transcribed using speech-to-text models. This gives you full-text search across everything anyone said in your footage. But it goes beyond exact word matching. Semantic understanding means searching "discussion about timeline delays" can surface a clip where someone says "we are running three weeks behind schedule."
Object detection
Computer vision models identify objects in every frame: people, cars, laptops, coffee cups, animals, furniture, signage. You can search for "dog" and find every clip where a dog appears on screen, regardless of whether anyone mentioned it.
Face recognition
Faces get detected and clustered across your entire library. Once you identify a person, you can search for every clip they appear in. This works across different cameras, lighting conditions, and angles.
Scene descriptions
AI generates natural-language descriptions of what is happening in each scene. "Two people sitting at a conference table with a whiteboard behind them." "Close-up of hands typing on a keyboard." "Aerial shot of a city at night." These descriptions become searchable, so you can find footage by describing what you need in plain English.
Visual similarity
Beyond specific objects, semantic search can match the overall feel and composition of a scene. Searching "moody low-key lighting" or "bright outdoor interview" works because the search model understands visual concepts, not just labels.
Why this matters for editors
Editors spend a shocking amount of time looking for footage. Not editing it. Not colour grading it. Not sound mixing it. Just finding the right clip.
Industry estimates put the time spent searching for assets at 20 to 30 percent of total post-production time. On a project with 50 hours of source footage, that could mean days of scrubbing through clips before the real editing work even starts.
Semantic video search compresses that process. Instead of scrubbing, you describe what you need and get results in seconds. The search understands your intent, not just your exact words.
Before semantic search
- Open a project bin with 400 clips
- Scrub through clips looking for the right shot
- Check three other project folders because you are not sure which shoot it was from
- Find something close enough
- Repeat for the next shot
After semantic search
- Type "wide shot of factory floor with workers"
- Get ranked results across your entire library
- Preview and select
- Export to your timeline
The difference is not incremental. It is a fundamentally different workflow.
Multimodal means fewer blind spots
The power of semantic video search comes from combining multiple modalities. A keyword-based system might miss a clip because it was not tagged correctly. A transcript-only search misses anything that was shown but not said. An object-detection-only system misses the context of what is happening.
By combining speech, objects, faces, scene descriptions, and visual understanding, semantic search covers the blind spots that any single approach would leave open.
Example: You search for "CEO presenting quarterly results."
- Transcript search finds clips where someone says "quarterly results" but does not know if the CEO is the one speaking.
- Face recognition finds every clip the CEO appears in but does not know the topic.
- Object detection finds presentations and slides but does not know who is presenting or what about.
- Semantic search combines all of these. It finds clips where the CEO is visible, the topic is quarterly results (whether stated explicitly or implied), and the setting looks like a presentation. That is the clip you actually need.
What semantic search is not
It is worth being honest about the limitations.
Semantic search is not perfect recall. It will sometimes miss relevant clips, especially if the visual or audio signal is ambiguous. A very brief appearance of an object or a mumbled mention in dialogue might not surface.
It is not a replacement for editorial judgement. The search ranks results by relevance, but "relevant" does not mean "right for your edit." You still need to watch the clips and decide what works creatively.
For most editing workflows, "get me there in seconds" is dramatically better than "get me there in hours."
How FrameQuery implements it
FrameQuery is our implementation of semantic video search. It processes your footage through multiple AI models (transcription, object detection, face recognition, scene description) and stores the results in a local search index on your machine.
When you type a query, it searches across all of these modalities simultaneously. Results are ranked by relevance and presented with previews so you can quickly assess each match.
The key design decision is that search is local and free. You pay for the initial processing (the GPU-intensive work of analysing your footage), but once your index is built, you can search it as many times as you want with no network connection and no per-query cost.
Semantic video search is what happens when AI actually solves a real editing problem instead of generating B-roll you did not ask for. Join the waitlist to try it with your own footage.