Communications of the ACM
A fuzzy video content representation for video summarization and content-based retrieval
Signal Processing - Special issue on fuzzy logic in signal processing
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Context and Memory in Multimedia Content Analysis
IEEE MultiMedia
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
User-adaptive home video summarization using personal photo libraries
Proceedings of the 6th ACM international conference on Image and video retrieval
Content-driven adaptation of on-line video
Image Communication
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
Hierarchical modeling and adaptive clustering for real-time summarization of rush videos
IEEE Transactions on Multimedia
Two-stage hierarchical video summary extraction to match low-level user browsing preferences
IEEE Transactions on Multimedia
Personalized abstraction of broadcasted American football video by highlight selection
IEEE Transactions on Multimedia
Affective video content representation and modeling
IEEE Transactions on Multimedia
Fast similarity search and clustering of video sequences on the world-wide-web
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
Adaptive extraction of highlights from a sport video based on excitement modeling
IEEE Transactions on Multimedia
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Rate-distortion optimal video summary generation
IEEE Transactions on Image Processing
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Summarization of videotaped presentations: automatic analysis of motion and gesture
IEEE Transactions on Circuits and Systems for Video Technology
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Object-based video abstraction for video surveillance systems
IEEE Transactions on Circuits and Systems for Video Technology
Efficient video similarity measurement with video signature
IEEE Transactions on Circuits and Systems for Video Technology
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
MINMAX optimal video summarization
IEEE Transactions on Circuits and Systems for Video Technology
Information theory-based shot cut/fade detection and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
Towards Theoretical Performance Limits of Video Parsing
IEEE Transactions on Circuits and Systems for Video Technology
A Multiple Visual Models Based Perceptive Analysis Framework for Multilevel Video Summarization
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided.