期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 6, 页码 1867-1878出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2413294
关键词
Complex event detection; concept selection; event oriented dictionary learning; supervised multi-task dictionary learning
资金
- Ministero dell'Istruzione, dell'Universita e della Ricerca Cluster Project Active Ageing at Home
- European Commission Project xLiMe
- Australian Research Council Discovery Projects
- U.S. Army Research Office [W911NF-13-1-0277]
- National Science Foundation [IIS-1251187]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1251187] Funding Source: National Science Foundation
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 l(p)-norm dictionary learning framework. Extensive experimental results on TRECVID multimedia event detection dataset demonstrate the efficacy of our proposed method.
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