Visual Content Search Over Videos: Enhancing YouTube Search 🐢

Imagine the ability to search through YouTube videos, not by the text in the titles or descriptions, but by the actual content of the video frames themselves. This is precisely what the “Visual Content Search Over Videos” project accomplishes. Utilising various AI tools and libraries, this project offers a more precise and in-depth approach to video searching.

Give it a try 🚀

Project Overview

The Visual Content Search Over Videos project allows users to enter a text string, which then prompts a search across YouTube videos based on the actual visual content. What sets this project apart is that the search mechanism sifts through the frames of the videos rather than relying on the associated text. As a result, the search accuracy is significantly enhanced, providing users with a list of videos relevant to their search query, ranked by relevance. Users can click on any video from the list to play it, being redirected to the associated timestamp.

Tools Utilised

This project harnesses several advanced AI tools and libraries to accomplish its functionality. The key tools used in this project include:

1. CLIP (OpenAI): CLIP (Contrastive Language-Image Pretraining) is an AI model developed by OpenAI. It is designed to understand images in conjunction with natural language. In this project, CLIP is used to interpret the video frames in the context of the user’s text search string, facilitating accurate and relevant search results.

2. FAISS (Facebook): Developed by Facebook AI, FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. FAISS is employed in this project to match the user’s search query against a database of video frame embeddings, thereby enabling rapid and precise search.

3. HuggingFace Spaces: This is a platform that hosts Machine Learning models in a user-friendly manner. HuggingFace Spaces is instrumental in creating an accessible interface where users can easily input their search terms.

4. Streamlit: A popular open-source app framework for Machine Learning and Data Science projects. Streamlit is used for its simplicity and versatility to create the user interface of this application.

The project runs on the Streamlit SDK version 1.19.0. For those looking to take a deep dive into this project, the source code can be accessed and cloned from the project’s HuggingFace repository. The main application file is video_semantic_search/app.py.

Conclusion

Visual Content Search Over Videos represents a different approach in the realm of video content searching. Leveraging new AI tools, this project is an excellent example of the potential of AI to transform our interaction with digital content, offering a fresh and accurate approach to scouring video content.

This project, along with others like it, marks an exciting era where AI continues to redefine our boundaries and capabilities. It is a testament to the power of machine learning and the innovation it continues to inspire.