Semantic Search Tool for Video

EasyChair Preprint 12503

Abstract

To enhance content retrieval and discovery, a new semantic search tool for videos has been created. With the use of strong algorithms and machine learning techniques, the system can intelligently analyze video data to extract relevant relationships and context. Compared to conventional methods, dynamic semantic indexing yields more accurate and context-aware search results. Furthermore, the system gradually adapts to choices and comments, giving priority to user engagement. Following rigorous testing, the semantic search engine outperforms traditional video search methods in terms of recall and precision. This innovation contributes to the evolving field of video retrieval and encourages a more efficient and user-centered experience.

Keyphrases: Content Retrieval, Dynamic Semantic Indexing, Machine Learning Algorithms, User-centered Experience, context-aware search, recall and precision, search results, video retrieval

@booklet{EasyChair:12503,
  author    = {Ritik Mendapara and Dishit Jayswal and Het Kasundra and Yash Patel and Akshara Prachi},
  title     = {Semantic Search Tool for Video},
  howpublished = {EasyChair Preprint 12503},
  year      = {EasyChair, 2024}}