title: Event oriented dictionary learning for complex event detection.
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.
authors: Yan
authors: Y
authors: Yan
authors: Yi
authors: Y
authors: Yang
authors: Deyu
authors: D
authors: Meng
authors: Gaowen
authors: G
authors: Liu
authors: Wei
authors: W
authors: Tong
authors: Alexander G
authors: AG
authors: Hauptmann
authors: Nicu
authors: N
authors: Sebe