BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient and cost-effective techniques for browsing and indexing large video databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On clustering and retrieval of video shots
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Constructing table-of-content for videos
Multimedia Systems - Special section on video libraries
Rapid serial visual presentation techniques for consumer digital video devices
Proceedings of the 16th annual ACM symposium on User interface software and technology
Advanced user interfaces for dynamic video browsing
Proceedings of the 12th annual ACM international conference on Multimedia
Hypervideo expression: experiences with hyper-hitchcock
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
Mixed-initiative photo collage authoring
MM '08 Proceedings of the 16th ACM international conference on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Hierarchical browsing of video key frames
ECIR'07 Proceedings of the 29th European conference on IR research
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Hierarchical video browsing with a 3D carousel
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Although people are capturing more video with their mobile phones, digital cameras, and other devices, they rarely watch all that video. More commonly, users extract a still image from the video to print or a short clip to share with others. We created a novel interface for browsing through a video keyframe hierarchy to find frames or clips. The interface is shown to be more efficient than scrolling linearly through all keyframes. We developed algorithms for selecting quality keyframes and for clustering keyframes hierarchically. At each level of the hierarchy, a single representative keyframe from each cluster is shown. Users can drill down into the most promising cluster and view representative keyframes for the sub-clusters. Our clustering algorithms optimize for short navigation paths to the desired keyframe. A single keyframe is located using a non-temporal clustering algorithm. A video clip is located using one of two temporal clustering algorithms. We evaluated the clustering algorithms using a simulated search task. User feedback provided us with valuable suggestions for improvements to our system.