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.
Notably, we applied our previous work on concept discovery from Internet images @cite_30 as a building component of our concept library construction process. This previous work aims at discovering concepts from a pre-specified set of event definitions within a known domain and studying the large beneficial impacts of such event-specific concepts in event retrieval, zero-example detection, and summarization. In contrast, this paper focuses on discovering semantic concepts from an external event knowledge base, WikiHow, and using its rich ontological hierarchy to organize the large number of concepts learned from the Web. Such ontological structure plays an important role in handling novel events that have not been seen in the learning stage, as confirmed by the significant performance gains reported in the experiments.
@cite_30
2048261712 2035607533 2112856309 2226011429
Analysis and detection of complex events in videos require a semantic representation of the video content. Existing video semantic representation methods typically require users to pre-define an exhaustive concept lexicon and manually annotate the presence of the concepts in each video, which is infeasible for real-world video event detection problems. In this paper, we propose an automatic semantic concept discovery scheme by exploiting Internet images and their associated tags. Given a target event and its textual descriptions, we crawl a collection of images and their associated tags by performing text based image search using the noun and verb pairs extracted from the event textual descriptions. The system first identifies the candidate concepts for an event by measuring whether a tag is a meaningful word and visually detectable. Then a concept visual model is built for each candidate concept using a SVM classifier with probabilistic output. Finally, the concept models are applied to generate concept based video representations. We use the TRECVID Multimedia Event Detection (MED) 2013 as our video test set and crawl 400K Flickr images to automatically discover 2, 000 visual concepts. We show significant performance gains of the proposed concept discovery method over different video event detection tasks including supervised event modeling over concept space and semantic based zero-shot retrieval without training examples. Importantly, we show the proposed method of automatic concept discovery outperforms other well-known concept library construction approaches such as Classemes and ImageNet by a large margin (228 ) in zero-shot event retrieval. Finally, subjective evaluation by humans also confirms clear superiority of the proposed method in discovering concepts for event representation. Event-specific concepts are the semantic concepts specifically designed for the events of interest, which can be used as a mid-level representation of complex events in videos. Existing methods only focus on defining event-specific concepts for a small number of pre-defined events, but cannot handle novel unseen events. This motivates us to build a large scale event-specific concept library that covers as many real-world events and their concepts as possible. Specifically, we choose WikiHow, an online forum containing a large number of how-to articles on human daily life events. We perform a coarse-to-fine event discovery process and discover 500 events from WikiHow articles. Then we use each event name as query to search YouTube and discover event-specific concepts from the tags of returned videos. After an automatic filter process, we end up with 95,321 videos and 4,490 concepts. We train a Convolutional Neural Network (CNN) model on the 95,321 videos over the 500 events, and use the model to extract deep learning feature from video content. With the learned deep learning feature, we train 4,490 binary SVM classifiers as the event-specific concept library. The concepts and events are further organized in a hierarchical structure defined by WikiHow, and the resultant concept library is called EventNet. Finally, the EventNet concept library is used to generate concept based representation of event videos. To the best of our knowledge, EventNet represents the first video event ontology that organizes events and their concepts into a semantic structure. It offers great potential for event retrieval and browsing. Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the proposed EventNet concept library consistently and significantly outperforms the state-of-the-art (such as the 20K ImageNet concepts trained with CNN) by a large margin up to 207 . We will also show that EventNet structure can help users find relevant concepts for novel event queries that cannot be well handled by conventional text based semantic analysis alone. The unique two-step approach of first applying event detection models followed by detection of event-specific concepts also provides great potential to improve the efficiency and accuracy of Event Recounting since only a very small number of event-specific concept classifiers need to be fired after event detection. 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. Complex event retrieval is a challenging research problem, especially when no training videos are available. An alternative to collecting training videos is to train a large semantic concept bank a priori. Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the concept responses on unseen videos. However, defining an exhaustive concept lexicon and pre-training it requires vast computational resources. Therefore, recent approaches automate concept discovery and training by leveraging large amounts of weakly annotated web data. Compact visually salient concepts are automatically obtained by the use of concept pairs or, more generally, n-grams. However, not all visually salient n-grams are necessarily useful for an event query--some combinations of concepts may be visually compact but irrelevant--and this drastically affects performance. We propose an event retrieval algorithm that constructs pairs of automatically discovered concepts and then prunes those concepts that are unlikely to be helpful for retrieval. Pruning depends both on the query and on the specific video instance being evaluated. Our approach also addresses calibration and domain adaptation issues that arise when applying concept detectors to unseen videos. We demonstrate large improvements over other vision based systems on the TRECVID MED 13 dataset.