Near-lossless video summarization
MM '09 Proceedings of the 17th ACM international conference on Multimedia
VideoSense: a contextual in-video advertising system
IEEE Transactions on Circuits and Systems for Video Technology
No-reference image quality assessment in contourlet domain
Neurocomputing
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Visual quality assessment for web videos
Journal of Visual Communication and Image Representation
Memory matrix: a novel user experience for home video
Proceedings of the international conference on Multimedia
Community discovery from movie and its application to poster generation
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Image search result summarization with informative priors
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Location based abstraction of user generated mobile videos
Image Communication
A hybrid matching algorithm based on contour and motion information for depth estimation
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Near-lossless semantic video summarization and its applications to video analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Compared with the video programs taken by professionals, home videos are always with low quality content resulted from non-professional capture skills. In this paper, we present a novel spatiotemporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, subshot, is selected as the basic unit for quality assessment. A set of spatiotemporal visual artifacts, regarded as the key factors affecting the overall perceived quality (i.e., unstableness and jerkiness as temporal factors; infidelity, blurring, brightness, and orientation as spatial factors), are mined from each subshot based on particular characteristics of home videos. The relationship between the overall quality metric and these factors are exploited by three different methods, including user study-based, rule-based and learning-based. To validate the proposed scheme, we present a scalable quality-based home video summarization system from a novel perspective-achieving the best visual quality while simultaneously preserving the most informative content. A comparison user study between this system and the attention model-based video skimming approach demonstrated the effectiveness of the proposed quality assessment scheme