Which Information Sources are More Effective and Reliable in Video Search

Zhiyong Cheng, Xuanchong Li, Jialie Shen, Alexander G. Hauptmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

It is common that users are interested in finding video segments, which contain further information about the video contents in a segment of interest. To facilitate users to find and browse related video contents, video hyperlinking aims at constructing links among video segments with relevant information in a large video collection. In this study, we explore the effectiveness of various video features on the performance of video hyperlinking, including subtitle, metadata, content features (i.e., audio and visual), surrounding context, as well as the combinations of those features. Besides, we also test different search strategies over different types of queries, which are categorized according to their video contents. Comprehensive experimental studies have been conducted on the dataset of TRECVID 2015 video hyperlinking task. Results show that (1) text features play a crucial role in search performance, and the combination of audio and visual features cannot provide improvements; (2) the consideration of contexts cannot obtain better results; and (3) due to the lack of training examples, machine learning techniques cannot improve the performance.
Original languageEnglish
Title of host publicationProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Place of PublicationNew York, USA
PublisherAssociation for Computing Machinery (ACM)
Pages1069-1072
ISBN (Print)978-1-4503-4069-4
DOIs
Publication statusPublished - 22 Jul 2016

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