Multimedia Internet of Things: A Comprehensive Survey <s> D. EVENT PROCESSING <s> Events are real-world occurrences that unfold over space and time. Event mining from multimedia streams improves the access and reuse of large media collections, and it has been an active area of research with notable progress. This paper contains a survey on the problems and solutions in event mining, approached from three aspects: event description, event-modeling components, and current event mining systems. We present a general characterization of multimedia events, motivated by the maxim of five ldquoWrdquos and one ldquoHrdquo for reporting real-world events in journalism: when, where, who, what, why, and how. We discuss the causes for semantic variability in real-world descriptions, including multilevel event semantics, implicit semantics facets, and the influence of context. We discuss five main aspects of an event detection system. These aspects are: the variants of tasks and event definitions that constrain system design, the media capture setup that collectively define the available data and necessary domain assumptions, the feature extraction step that converts the captured data into perceptually significant numeric or symbolic forms, statistical models that map the feature representations to richer semantic descriptions, and applications that use event metadata to help in different information-seeking tasks. We review current event-mining systems in detail, grouping them by the problem formulations and approaches. The review includes detection of events and actions in one or more continuous sequences, events in edited video streams, unsupervised event discovery, events in a collection of media objects, and a discussion on ongoing benchmark activities. These problems span a wide range of multimedia domains such as surveillance, meetings, broadcast news, sports, documentary, and films, as well as personal and online media collections. We conclude this survey with a brief outlook on open research directions. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> D. EVENT PROCESSING <s> In this paper, we propose a new approach to classify and rank multimedia events based purely on audio content using video data from TRECVID-2013 multimedia event detection (MED) challenge. We perform several layers of nonlinear mappings to extract a set of unsupervised features from an initial set of temporal and spectral features to obtain a superior presentation of the atomic audio units. Additionally, we propose a novel weighted divergence measure for kernel based classifiers. The extensive set of experiments confirms that augmentation of the proposed steps results in an improved accuracy for most of the event classes. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> D. EVENT PROCESSING <s> Language content in videos from speech and overlaid or inscene video text can provide high precision signals for video event detection and retrieval. However, sporadic occurrence, content that is unrelated to the events of interest, and high error rates of current speech and text recognition systems on consumer domain video make it difficult to exploit these channels. In this paper, we study different representations of language content to address these challenges. First, we utilize likelihood weighted word lattices obtained from a Hidden Markov Model (HMM) based decoding engine to encode many alternate hypotheses, rather than relying on noisy single best hypotheses. Second, we utilize an event-independent modified term frequency-inverse document frequency (TF-IDF) weighting scheme to obtain the final feature vector. We present detailed experimental results on the TRECVID MED 2013 dataset containing ~150000 videos, and show that our representation significantly outperforms alternate representations for both speech and video text. <s> BIB003 </s> Multimedia Internet of Things: A Comprehensive Survey <s> D. EVENT PROCESSING <s> Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED. <s> BIB004
Events are defined as real-world occurrences that reveal over space and time. A huge volume of event content is being captured in multimedia, of which YouTube videos, TV shows, and animations cover 60% of the Internet traffic  . The tremendous increase in real-life events captured in multimedia requires event analysis to associate metadata with events for efficient query processing. Event detection BIB002 , feature representation BIB003 , and adding semantics BIB004 , is known as event mining BIB001 . An intelligent event mining in audio, videos, and images are required in M-IoT for efficient resource utilization to process the required service with minimum delay.