Video similarity search identifies and retrieves videos that are similar to a given input video. The system analyzes key features such as objects, motion, color patterns, and even audio to measure similarity. These features are encoded into vectors, allowing fast comparison and retrieval from large video datasets.
Applications of video similarity search include detecting duplicate content, recommending related videos, and verifying intellectual property. For example, platforms like YouTube use this technology to find and remove pirated videos or recommend similar content based on user preferences.
With advancements in deep learning, video similarity search has become more accurate and scalable, capable of handling diverse datasets across industries.