Video summarization by curve simplification
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Video Manga: generating semantically meaningful video summaries
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Modelling subjectivity in visual perception of orientation for image retrieval
Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
Dynamic selection and effective compression of key frames for video abstraction
Pattern Recognition Letters
Keyframe-based video summarization using Delaunay clustering
International Journal on Digital Libraries
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
STIMO: STIll and MOving video storyboard for the web scenario
Multimedia Tools and Applications
Keyframe-Based Video Summary Using Visual Attention Clues
IEEE MultiMedia
VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method
Pattern Recognition Letters
Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Key frame extraction based on visual attention model
Journal of Visual Communication and Image Representation
Image Signature: Highlighting Sparse Salient Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
An attention-based decision fusion scheme for multimedia information retrieval
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video
IEEE Transactions on Multimedia
Video retrieval based on scene change detection in compressed streams
IEEE Transactions on Consumer Electronics
Automatic video summarizing tool using MPEG-7 descriptors for personal video recorder
IEEE Transactions on Consumer Electronics
A video summarization tool using two-level redundancy detection for personal video recorders
IEEE Transactions on Consumer Electronics
Hierarchical video summarization in reference subspace
IEEE Transactions on Consumer Electronics
PVR: a novel PVR scheme for content protection
IEEE Transactions on Consumer Electronics
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
Combined key-frame extraction and object-based video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Multimedia
Adaptive key frame extraction for video summarization using an aggregation mechanism
Journal of Visual Communication and Image Representation
Computers in Biology and Medicine
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The huge amount of video data on the internet requires efficient video browsing and retrieval strategies. One of the viable solutions is to provide summaries of the videos in the form of key frames. The video summarization using visual attention modeling has been used of late. In such schemes, the visually salient frames are extracted as key frames on the basis of theories of human attention modeling. The visual attention modeling schemes have proved to be effective in video summarization. However, the high computational costs incurred by these techniques limit their applicability in practical scenarios. In this context, this paper proposes an efficient visual attention model based key frame extraction method. The computational cost is reduced by using the temporal gradient based dynamic visual saliency detection instead of the traditional optical flow methods. Moreover, for static visual saliency, an effective method employing discrete cosine transform has been used. The static and dynamic visual attention measures are fused by using a non-linear weighted fusion method. The experimental results indicate that the proposed method is not only efficient, but also yields high quality video summaries.