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|Title:||A generic framework for event detection in various video domains||Authors:||Zhang, T.
multiple instance learning
|Issue Date:||2010||Citation:||Zhang, T.,Xu, C.,Zhu, G.,Liu, S.,Lu, H. (2010). A generic framework for event detection in various video domains. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 103-112. ScholarBank@NUS Repository. https://doi.org/10.1145/1873951.1873967||Abstract:||Event detection is essential for the extensively studied video analysis and understanding area. Although various approaches have been proposed for event detection, there is a lack of a generic event detection framework that can be applied to various video domains (e.g. sports, news, movies, surveillance). In this paper, we present a generic event detection approach based on semi-supervised learning and Internet vision. Concretely, a Graph-based Semi-Supervised Multiple Instance Learning (GSSMIL) algorithm is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the event models to detect video event boundaries. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e.g. movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e.g. YouTube) in order to give more distributive information for event modeling. A critical issue of GSSMIL in constructing a graph is the weight assignment, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers. We perform the thorough experiments in three popular video domains: movies, sports and news. The results compared with the state-of-the-arts are promising and demonstrate our proposed approach is performance-effective. © 2010 ACM.||Source Title:||MM'10 - Proceedings of the ACM Multimedia 2010 International Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/68823||ISBN:||9781605589336||DOI:||10.1145/1873951.1873967|
|Appears in Collections:||Staff Publications|
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