Event oriented dictionary learning for complex event detection

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Yan YanNicu Sebe

Abstract

Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained Internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose a novel strategy to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Toward this goal, we leverage training images (frames) of selected concepts from the semantic indexing dataset with a pool of 346 concepts, into a novel supervised multitask lp -norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method.

References

Dec 13, 2006·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Michael Elad, Michal Aharon
Nov 2, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yan YanNicu Sebe

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Citations

Jun 13, 2015·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yan YanNicu Sebe
Feb 6, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Wei WangNicu Sebe
Jun 24, 2017·IEEE Transactions on Neural Networks and Learning Systems·Zhigang MaAlexander G Hauptmann
Mar 30, 2016·Journal of Medical Systems·Zhihan LvPablo Gagliardo

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