Principles of visual information retrieval
Principles of visual information retrieval
A Maximum-Likelihood Strategy for Directing Attention during Visual Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Live sports event detection based on broadcast video and web-casting text
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Affective video content representation and modeling
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
IEEE Transactions on Circuits and Systems for Video Technology
An HMM-based framework for video semantic analysis
IEEE Transactions on Circuits and Systems for Video Technology
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Attention is a psychological measurement of human reflection against stimulus. We propose a general framework of highlight detection by comparing attention intensity during the watching of sports videos. Three steps are involved: adaptive selection on salient features, unified attention estimation and highlight identification. Adaptive selection computes feature correlation to decide an optimal set of salient features. Unified estimation combines these features by the technique of multi-resolution auto-regressive (MAR) and thus creates a temporal curve of attention intensity. We rank the intensity of attention to discriminate boundaries of highlights. Such a framework alleviates semantic uncertainty around sport highlights and leads to an efficient and effective highlight detection. The advantages are as follows: (1) the capability of using data at coarse temporal resolutions; (2) the robustness against noise caused by modality asynchronism, perception uncertainty and feature mismatch; (3) the employment of Markovian constrains on content presentation, and (4) multi-resolution estimation on attention intensity, which enables the precise allocation of event boundaries.