How to Find Any Scene in Your Videos by Describing It
Finding that one moment inside hours of footage is frustrating: filenames don't help and scrubbing the timeline by hand wastes time. Semantic search fixes this by searching for visual meaning, not filename text.
Search by meaning, not by name
Traditional search compares text strings. Semantic search compares concepts: it turns both your video frames and your description into numeric vectors and finds the closest matches.
In practice you type "a person walking on the beach" and the system takes you to the clips that actually show that scene — even if the file is named "IMG_4521.mov".
How CLIP does it, locally
AI Video Scanner Pro uses OpenAI CLIP, a model that places images and text in the same vector space. During scanning it extracts key frames and computes their embeddings; when you search, it embeds your phrase and ranks results by similarity.
All of this happens on your Mac: the embeddings stay in the local database and no frame is uploaded.
Tips for effective searches
- Describe the scene the way you'd say it out loud: subject, action, setting.
- Combine with object tags (e.g. "car") and color (e.g. "red 30") to narrow down.
- Click a result to jump to the exact timestamp in the player.
Frequently asked
Do I have to train anything or label my videos?
No. The AI scan automatically generates tags and embeddings. From then on semantic search works across your whole archive with no configuration.
Does it work with plain English?
Yes, you describe scenes in natural language. For best results, simple and concrete descriptions work better than very long sentences.
Are my videos uploaded to perform the search?
No. Indexing and search happen entirely locally; the embeddings are stored on your Mac.
Your footage. Your Mac. Your rules.
Index, search and transcribe your entire library without uploading a single frame. One-time price, 10-day free trial, macOS 13+.
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