Concept-based video representation has proven to be eective in complex event detection. However, existing methods either manually design concepts or directly adopt concept libraries not specically designed for events. In this paper, we propose to build Concept Bank, the largest concept library consisting of 4; 876 concepts specically designed to cover 631 real-world events. To construct the Concept Bank, we rst gather a comprehensive event collection from WikiHow, a collaborative writing project that aims to build the world’s largest manual for any possible How-To event. For each event, we then search Flickr and discover relevant concepts from the tags of the returned images. We train a Multiple Kernel Linear SVM for each discovered concept as a concept detector in Concept Bank. We organize the concepts into a ve-layer tree structure, in which the higher-level nodes correspond to the event categories while the leaf nodes are the event-specic concepts discovered for each event. Based on such tree ontology, we develop a semantic matching method to select relevant concepts for each textual event query, and then apply the corresponding concept detectors to generate concept-based video representations. We use TRECVID Multimedia Event Detection 2013 and Columbia Consumer Video open source event denitions and videos as our test sets and show very promising results on two video event detection tasks: event modeling over concept space and zero-shot event retrieval. To the best of our knowledge, this is the largest concept library covering the largest number of real-world events.
There are some recent works that try to perform event detection with semantic concepts. Yang @cite_18 applied deep belief nets to group a large number of event video shots into a number of shot clusters, and then treat each cluster center as a data-driven concept. Then each video is mapped onto the cluster centers and encoded into a concept based representation. However, such data-driven concepts do not convey any semantic information and hence cannot be utilized for high-level semantic understanding. Liu @cite_1 manually defined concepts present in event videos and categorized them into object'', scene'' and action''. They annotated the presence of each concept on training videos and then built the individual concept detectors. Nevertheless, as aforementioned, this requires too many manual efforts, and is not applicable for real-world large-scale video event detection tasks. Different from these prior works, we focus on automatically discovering potential concepts present in any possible events with interpretable semantic meaning.
@cite_18 @cite_1
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Automatic event detection in a large collection of unconstrained videos is a challenging and important task. The key issue is to describe long complex video with high level semantic descriptors, which should find the regularity of events in the same category while distinguish those from different categories. This paper proposes a novel unsupervised approach to discover data-driven concepts from multi-modality signals (audio, scene and motion) to describe high level semantics of videos. Our methods consists of three main components: we first learn the low-level features separately from three modalities. Secondly we discover the data-driven concepts based on the statistics of learned features mapped to a low dimensional space using deep belief nets (DBNs). Finally, a compact and robust sparse representation is learned to jointly model the concepts from all three modalities. Extensive experimental results on large in-the-wild dataset show that our proposed method significantly outperforms state-of-the-art methods. We propose to use action, scene and object concepts as semantic attributes for classification of video events in InTheWild content, such as YouTube videos. We model events using a variety of complementary semantic attribute features developed in a semantic concept space. Our contribution is to systematically demonstrate the advantages of this concept-based event representation (CBER) in applications of video event classification and understanding. Specifically, CBER has better generalization capability, which enables to recognize events with a few training examples. In addition, CBER makes it possible to recognize a novel event without training examples (i.e., zero-shot learning). We further show our proposed enhanced event model can further improve the zero-shot learning. Furthermore, CBER provides a straightforward way for event recounting understanding. We use the TRECVID Multimedia Event Detection (MED11) open source event definitions and datasets as our test bed and show results on over 1400 hours of videos.