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.
Video event detection has became an important research area in computer vision literature. Natarajan @cite_5 investigated the multimodal fusion of low-level video features and the extracted videotext information in video content, and achieved good performance on the detection. Ye @cite_7 discovered the bi-modal audio-visual codewords and leveraged the joint patterns across the audio and visual space to boost event detection performance. Duan @cite_22 incorporated the web sources videos crawled from Youtube to relieve the insufficiency of the number of training videos of an event, and developed a cross-domain video event detection model. Tang @cite_23 developed a large margin framework to exploit the latent temporal structure in successive clips of a long event video. These excellent works focus on modeling events into sophisticated statistical models or fusing mutimodal information. However, none of them can reveal the rich semantics in event videos.
@cite_5 @cite_23 @cite_22 @cite_7
2141939040 2142258645 2160039895 2083270451
Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams. In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a conditional model trained in a max-margin framework that is able to automatically discover discriminative and interesting segments of video, while simultaneously achieving competitive accuracies on difficult detection and recognition tasks. We introduce latent variables over the frames of a video, and allow our algorithm to discover and assign sequences of states that are most discriminative for the event. Our model is based on the variable-duration hidden Markov model, and models durations of states in addition to the transitions between states. The simplicity of our model allows us to perform fast, exact inference using dynamic programming, which is extremely important when we set our sights on being able to process a very large number of videos quickly and efficiently. We show promising results on the Olympic Sports dataset [16] and the 2011 TRECVID Multimedia Event Detection task [18]. We also illustrate and visualize the semantic understanding capabilities of our model. Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods. Joint audio-visual patterns often exist in videos and provide strong multi-modal cues for detecting multimedia events. However, conventional methods generally fuse the visual and audio information only at a superficial level, without adequately exploring deep intrinsic joint patterns. In this paper, we propose a joint audio-visual bi-modal representation, called bi-modal words. We first build a bipartite graph to model relation across the quantized words extracted from the visual and audio modalities. Partitioning over the bipartite graph is then applied to construct the bi-modal words that reveal the joint patterns across modalities. Finally, different pooling strategies are employed to re-quantize the visual and audio words into the bi-modal words and form bi-modal Bag-of-Words representations that are fed to subsequent multimedia event classifiers. We experimentally show that the proposed multi-modal feature achieves statistically significant performance gains over methods using individual visual and audio features alone and alternative multi-modal fusion methods. Moreover, we found that average pooling is the most suitable strategy for bi-modal feature generation.