Semantic based background music recommendation for home videos

Yin Tzu Lin, Tsung Hung Tsai, Min Chun Hu, Wen-Huang Cheng, Ja Ling Wu

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

3 Scopus citations


In this paper, we propose a new background music recommendation scheme for home videos and two new features describing the short-term motion/tempo distribution in visual/aural content. Unlike previous researches that merely matched the visual and aural contents through a perceptual way, we incorporate the textual semantics and content semantics while determining the matching degree of a video and a song. The key idea is that the recommended music should contain semantics that relate to the ones in the input video and that the rhythm of the music and the visual motion of the video should be harmonious enough. As a result, a few user-given tags and automatically annotated tags are used to compute their relation to the lyrics of the songs for selecting candidate musics. Then, we use the proposed motion-direction histogram (MDH) and pitch tempo pattern (PTP) to do the second-run selection. The user preference to the music genre is also taken into account as a filtering mechanism at the beginning. The primitive user evaluation shows that the proposed scheme is promising.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings
Number of pages8
EditionPART 2
StatePublished - 7 Feb 2014
Event20th Anniversary International Conference on MultiMedia Modeling, MMM 2014 - Dublin, Ireland
Duration: 6 Jan 201410 Jan 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8326 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th Anniversary International Conference on MultiMedia Modeling, MMM 2014

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