Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Determining computable scenes in films and their structures using audio-visual memory models
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Introduction to algorithms
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for video scene boundary detection
Proceedings of the tenth ACM international conference on Multimedia
Automatic Scene Detection in News Program by Integrating Visual Feature and Rules
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Structuring home video by snippet detection and pattern parsing
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Intention-based home video browsing
Proceedings of the 13th annual ACM international conference on Multimedia
Modeling Intent for Home Video Repurposing
IEEE MultiMedia
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
User-adaptive home video summarization using personal photo libraries
Proceedings of the 6th ACM international conference on Image and video retrieval
On the range maximum-sum segment query problem
Discrete Applied Mathematics
Broadcast news story segmentation using social network analysis and hidden markov models
Proceedings of the 15th international conference on Multimedia
Proceedings of the 15th international conference on Multimedia
Authoring, viewing, and generating hypervideo: An overview of Hyper-Hitchcock
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Using cross-media correlation for scene detection in travel videos
Proceedings of the ACM International Conference on Image and Video Retrieval
Scene detection in videos using shot clustering and sequence alignment
IEEE Transactions on Multimedia
Exploiting external knowledge to improve video retrieval
Proceedings of the international conference on Multimedia information retrieval
SIEVE: search images effectively through visual elimination
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Aesthetics-based automatic home video skimming system
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Travel Video Scene Detection by Search
PSIVT '10 Proceedings of the 2010 Fourth Pacific-Rim Symposium on Image and Video Technology
Systematic evaluation of logical story unit segmentation
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Finding structure in home videos by probabilistic hierarchical clustering
IEEE Transactions on Circuits and Systems for Video Technology
Optimization-based automated home video editing system
IEEE Transactions on Circuits and Systems for Video Technology
Semantic Analysis for Automatic Event Recognition and Segmentation of Wedding Ceremony Videos
IEEE Transactions on Circuits and Systems for Video Technology
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We conduct video scene detection with the aids of web-based context, especially for travel videos captured by amateur photographers in journeys. Correlations between personal videos and predefined travel schedules, which are used to retrieve related data from general-purpose image/video search engines, are discovered. Because scene boundaries are clearly defined in travel schedules, we segment videos into scenes by checking the discovered cross-media correlation. To make different modalities comparable, keyframes extracted from videos and images retrieved from web are represented by visual word histograms, and the problem of correlation determination is then transformed as an approximate sequence matching problem. We prioritize different visual words according to statistics of retrieved data, and evaluate similarity between images based on the weighting scheme. To systematically determine scene boundaries after finding cross-media correlation, we introduce an energy minimization framework to jointly consider visual, temporal, and context information. Experimental results verify the effectiveness of the proposed idea, and show that it is promising to utilize cross-media correlation and web-based context in media analysis.