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.
Large scale video retrieval commonly employs a concept-based video representation (CBRE) @cite_2 @cite_3 @cite_4 @cite_17 , especially when only few or no training examples of the events are available. In this setting, complex events are represented in terms of a large set of concepts that are either event-driven (generated once the event description is known) @cite_12 @cite_30 @cite_1 or pre-defined @cite_22 @cite_11 @cite_5 . A test query description is mapped to a set of concepts whose detectors are then applied to videos to perform retrieval. However, methods based on pre-defined concepts need to train an exhaustive set of concept detectors a priori or the semantic gap between the query description and the concept database might be too large. This is computationally expensive and currently infeasible for real-world video retrieval systems. Instead, in this paper, given the textual description of the event to be retrieved, our approach leverages web image data to discover event-driven concepts and train detectors that are relevant to this specific event.
@cite_30 @cite_11 @cite_4 @cite_22 @cite_1 @cite_3 @cite_2 @cite_5 @cite_12 @cite_17
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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. 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. 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. Recent research in video retrieval has been successful at finding videos when the query consists of tens or hundreds of sample relevant videos for training supervised models. Instead, we investigate unsupervised zero-shot retrieval where no training videos are provided: a query consists only of a text statement. For retrieval, we use text extracted from images in the videos, text recognized in the speech of its audio track, as well as automatically detected semantically meaningful visual video concepts identified with widely varying confidence in the videos. In this work we introduce a new method for automatically identifying relevant concepts given a text query using the Markov Random Field (MRF) retrieval framework. We use source expansion to build rich textual representations of semantic video concepts from large external sources such as the web. We find that concept-based retrieval significantly outperforms text based approaches in recall. Using an evaluation derived from the TRECVID MED'11 track, we present early results that an approach using multi-modal fusion can compensate for inadequacies in each modality, resulting in substantial effectiveness gains. With relevance feedback, our approach provides additional improvements of over 50 .