Comparing Retrieval Effectiveness of Alternative Content Segmentation Methods for Internet Video Search

Maria Eskevich, Gareth J.F. Jones, Martha Larson, Christian Wartena, Robin Aly, Thijs Verschoor, Roeland Ordelman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Citations (Scopus)


We present an exploratory study of the retrieval of semiprofessional user-generated Internet video. The study is based on the MediaEval 2011 Rich Speech Retrieval (RSR) task for which the dataset was taken from the Internet sharing platform, and search queries associated with specific speech acts occurring in the video. We compare results from three participant groups using: automatic speech recognition system transcript (ASR), metadata manually assigned to each video by the user who uploaded it, and their combination. RSR 2011 was a known-item search for a single manually identified ideal jump-in point in the video for each query where playback should begin. Retrieval effectiveness is measured using the MRR and mGAP metrics. Using different transcript segmentation methods the participants tried to maximize the rank of the relevant item and to locate the nearest match to the ideal jump-in point. Results indicate that best overall results are obtained for topically homogeneous segments which have a strong overlap with the relevant region associated with the jump-in point, and that use of metadata can be beneficial when segments are unfocused or cover more than one topic.
Original languageEnglish
Title of host publicationProceedings of the 10th Workshop on Content-Based Multimedia Indexing (CMBI 2012)
Place of PublicationPiscataway, NJ
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)978-1-4673-2369-7
ISBN (Print)978-1-4673-2368-0
Publication statusPublished - Jun 2012

Publication series

NameProceedings International Workshop on Content-Based Multimedia Indexing (CBMI)
PublisherIEEE Computer Society
ISSN (Print)1949-3983
ISSN (Electronic)1949-3991

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