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Adaptive clustering and interactive visualizations to support the selection of video clips

Published: 18 April 2011 Publication History

Abstract

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.

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cover image ACM Conferences
ICMR '11: Proceedings of the 1st ACM International Conference on Multimedia Retrieval
April 2011
512 pages
ISBN:9781450303361
DOI:10.1145/1991996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 April 2011

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Author Tags

  1. adaptive clustering
  2. keyframe selection
  3. video browsing

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  • (2024)Improving Video Navigation for Spatial Task Tutorials by Spatially Segmenting and Situating How-To VideosProceedings of the 2024 ACM Symposium on Spatial User Interaction10.1145/3677386.3682103(1-13)Online publication date: 7-Oct-2024
  • (2024)SwapVid: Integrating Video Viewing and Document Exploration with Direct ManipulationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642515(1-13)Online publication date: 11-May-2024
  • (2019)The usefulness of multimedia surrogates for making relevance judgments about digital video objectsInformation Processing and Management: an International Journal10.1016/j.ipm.2019.10209156:6Online publication date: 1-Nov-2019
  • (2017)Interactive video search toolsMultimedia Tools and Applications10.1007/s11042-016-3661-276:4(5539-5571)Online publication date: 1-Feb-2017
  • (2016)Guiding Users through Asynchronous Meeting Content with Hypervideo Playback PlansProceedings of the 27th ACM Conference on Hypertext and Social Media10.1145/2914586.2914597(49-59)Online publication date: 10-Jul-2016
  • (2016)Summarizing video sequence using a graph-based hierarchical approachNeurocomputing10.1016/j.neucom.2015.08.057173:P3(1001-1016)Online publication date: 15-Jan-2016
  • (2015)Video Interaction ToolsACM Computing Surveys10.1145/280879648:1(1-34)Online publication date: 29-Sep-2015
  • (2015)Interactive Video SearchProceedings of the 23rd ACM international conference on Multimedia10.1145/2733373.2807417(1321-1322)Online publication date: 13-Oct-2015
  • (2015)3D Visualization of Multiscale Video Key FramesProceedings of the 2015 19th International Conference on Information Visualisation10.1109/iV.2015.48(223-227)Online publication date: 22-Jul-2015
  • (2015)Improving Interactive Known-Item Search in Video with the Keyframe Navigation TreeMultiMedia Modeling10.1007/978-3-319-14445-0_27(306-317)Online publication date: 2015
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