Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
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
Broadcast news story segmentation using social network analysis and hidden markov models
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)
Reranking Methods for Visual Search
IEEE MultiMedia
SIFT-Bag kernel for video event analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Aesthetics-based automatic home video skimming system
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Systematic evaluation of logical story unit segmentation
IEEE Transactions on Multimedia
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
Travelmedia: An intelligent management system for media captured in travel
Journal of Visual Communication and Image Representation
Travel photo and video summarization with cross-media correlation and mutual influence
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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Focusing on travel videos taken in uncontrolled environments and by amateur photographers, we exploit correlation between different modalities to facilitate effective travel video scene detection. Scenes in travel photos, i.e., content taken at the same scenic spot, can be easily determined by examining time information. For a travel video, we extract several keyframes for each video shot. Then, photos and keyframes are represented as a sequence of visual word histograms, respectively. Based on this representation, we transform scene detection into a sequence matching problem. After finding the best alignment between two sequences, we can determine scene boundaries in videos with the help of that in photos. We demonstrate that we averagely achieve a purity value of 0.95 if the proposed method is combined with conventional ones. We show that not only features of visual words aid in scene detection, but also cross-media correlation does.