Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Shortest Path Representation for Video Summarisation
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Efficient video indexing scheme for content-based retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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The aim of this work is to devise an effective method for static summarization of home video sequences. Based on the premise that the user watching a summary is interested in people related (how many, who, emotional state) or activity related aspects, we formulate a novel approach to video summarization that works to specifically expose relevant video frames that make the content spotting tasks possible. Unlike existing approaches, which work on low-level features which often produce the summary not appealing to the viewer due to the semantic gap between low-level features and high-level concepts, our approach is driven by various utility functions (identity count, identity recognition, emotion recognition, activity recognition, sense of space) that use the results of face detection, face clustering, shot clustering and within-cluster frame alignment. The summarization problem is then treated as the problem of extracting the set of keyframes that have the maximum combined utility.