Performance Comparisons of Content-Based Video Search Engine Retrieval Using Different Similarity Algorithms

Abstract Content-based video search engines (CBVSE) are broadly needed in many mainstream video search engines retrieving videos from public video streaming services over the Internet such as YouTube. As they are mostly text-based search engines that index and retrieve videos depending on the surrounding text around the video web page that contains information representing this video file. This paper is an attempt to improve the performance of a previously developed technique for a content-based video search engine designed to firstly index videos on YouTube and search and retrieve videos using a non-semantic video query. Moreover, a large-scale dataset was indexed containing more than 1088 YouTube video records. Each record contains a feature vector of the temporal set, key-objects sets, and keyframes representing video shots in each video file in addition to the URLs and other information gathered from each video file web page.