TY - JOUR AU - Adly, Ahmad Sedky AU - Hegazy, Islam AU - Elarif, Taha AU - Abdelwahab, M. S. PY - 2022/06/30 Y2 - 2024/03/29 TI - Development of an Effective Bootleg Videos Retrieval System as a Part of Content-Based Video Search Engine JF - International Journal of Computing JA - IJC VL - 21 IS - 2 SE - DO - 10.47839/ijc.21.2.2590 UR - https://www.computingonline.net/computing/article/view/2590 SP - 214-227 AB - <p>Many research studies in content-based video search engines are concerned with content-based video queries retrieval where a query by example is sent to retrieve a list of visually similar videos. However, minor research is concerned with indexing and searching public video streaming services such as YouTube, where there is a dilemma for misusing copyrighted video materials and detecting bootleg manipulated videos before being uploaded. In this paper, a novel and effective technique for a content-based video search engine with effective detection of bootleg videos is evaluated on a large-scale video index dataset of 1088 video records. A novel feature vector is introduced using video shots temporal and key-object/concept features applying combinational-based matching algorithms, using various similarity metrics for evaluation. The retrieval system was evaluated using more than 200 non-semantic-based video queries evaluating both normal and bootleg videos, with retrieval precision for normal videos of 97.9% and retrieval recall of 100% combined by the F1 measure to be 98.3%. Bootleg videos retrieval precision scored 99.2% and retrieval recall was of 96.7% combined by the F1 measure to be 97.9%. This allows making a conclusion that this technique can help in enhancing both traditional text-based search engines and commonly used bootleg detection techniques.</p> ER -