摘要 :
Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in multimedia...
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Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. It is widely used in many major potential applications like security and surveillance, entertainment, medicine, education programs and sports. The objective of video data mining is to discover and describe interesting patterns from the huge amount of video data as it is one of the core problem areas of the data-mining research community. Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining. Beginning with an overview of the video data-mining literature, this paper concludes with the applications of video mining.
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摘要 :
Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in mul-timedi...
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Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in mul-timedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. It is widely used in many major potential applications like security and sur-veillance, entertainment, medicine, education programs and sports. The objective of video data mining is to discover and describe interesting patterns from the huge amount of video data as it is one of the core problem areas of the data-mining research community. Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining. Beginning with an overview of the video data-mining literature, this paper concludes with the applications of video mining.
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Abstract With the rapid growth and popularity of YouTube, an increasing number of consumers rely on online product review videos to obtain product‐related information. As the provision of online review videos grows and consumers ...
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Abstract With the rapid growth and popularity of YouTube, an increasing number of consumers rely on online product review videos to obtain product‐related information. As the provision of online review videos grows and consumers increasingly rely on them for their purchase decisions, understanding factors that contribute to the perceived helpfulness of video reviews becomes critical for video review management. This paper examines how various visual and vocal characteristics of online review videos are associated with the perceived helpfulness of videos. We collect detailed observational data on 13,840 electronic product review videos posted on YouTube and employ video content analysis, speech recognition, and natural language processing techniques to extract the visual and vocal characteristics of review videos. By using econometric models, we find that the increase in visual stimulation, captured by brightness and visual dynamics, increases the perceived helpfulness of reviews. In addition, featuring reviewers’ faces in review videos increases the perceived helpfulness of videos. Consumers also perceive review videos in which reviewers express more positive facial emotions as more helpful. Furthermore, lower voice pitch and faster speech rates are associated with higher perceived helpfulness of reviews. To complement the empirical analysis and further isolate the causal effects of review brightness and pitch, we conduct controlled experiments. Overall, the findings can facilitate the management and operation of online review videos for product reviewers, businesses, review platforms, and consumers. In particular, the findings provide direct and actionable guidance to content generators who aim to create more helpful product reviews.
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In the UK alone there are currently over 4.2 million operational CCTV cameras, that is virtually one camera for every 14th person, and this figure is increasing at a fast rate throughout the world (especially after the tragic even...
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In the UK alone there are currently over 4.2 million operational CCTV cameras, that is virtually one camera for every 14th person, and this figure is increasing at a fast rate throughout the world (especially after the tragic events of 9/11 and 7/7) (Norris, McCahill, & Wood, 2004). Security concerns are not the only factor driving the rapid growth of CCTV cameras. Another important reason is the access of hidden knowledge extracted from CCTV footage to be used for effective business decision making, such as store designing, customer services, product marketing, reducing store shrinkage, etc. Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos (Anwar & Naftel, 2008). Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast, in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events (Aeasp); that is to discover events, which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector. The proposed event mining framework is an extension to our previous research work presented in Anwar et al. (2010) and also takes the temporal aspect of anomalous events against frequent sequence of events into consideration, that is to discover anomalous events which are true for a specific time interval only and might not be an anomalous events against frequent sequence of events over a whole time spectrum and vice versa. To confront the memory expensive process of searching all the instances of multiple sequential patterns in each data sequence an efficient dynamic sequential pattern search mechanism is introduced. Different experiments are conducted to evaluate the proposed anomalous events against frequent sequence of events mining algorithm's accuracy and performance.
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Abstract Context The game industry is increasingly growing in recent years. Every day, millions of people play video games, not only as a hobby, but also for professional competitions ( e.g., e-sports or speed-running) or for maki...
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Abstract Context The game industry is increasingly growing in recent years. Every day, millions of people play video games, not only as a hobby, but also for professional competitions ( e.g., e-sports or speed-running) or for making business by entertaining others ( e.g., streamers). The latter daily produce a large amount of gameplay videos in which they also comment live what they experience. But no software and, thus, no video game is perfect: Streamers may encounter several problems (such as bugs, glitches, or performance issues) while they play. Also, it is unlikely that they explicitly report such issues to developers. The identified problems may negatively impact the user’s gaming experience and, in turn, can harm the reputation of the game and of the producer.Objective In this paper, we propose and empirically evaluate GELID, an approach for automatically extracting relevant information from gameplay videos by (i) identifying video segments in which streamers experienced anomalies; (ii) categorizing them based on their type ( e.g., logic or presentation); clustering them based on (iii) the context in which appear ( e.g., level or game area) and (iv) on the specific issue type ( e.g., game crashes).Method We manually defined a training set for step 2 of GELID (categorization) and a test set for validating in isolation the four components of GELID. In total, we manually segmented, labeled, and clustered 170 videos related to 3 video games, defining a dataset containing 604 segments.Results While in steps 1 (segmentation) and 4 (specific issue clustering) GELID achieves satisfactory results, it shows limitations on step 3 (game context clustering) and, above all, step 2 (categorization).
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Marketers are becoming increasingly reliant on videos to market their products and services. However, there is no standard set of measures of visual information that can be applied to large datasets. This paper proposes two standa...
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Marketers are becoming increasingly reliant on videos to market their products and services. However, there is no standard set of measures of visual information that can be applied to large datasets. This paper proposes two standard measures that can be automatically obtained from videos: visual variation and video content. The paper tests the measures on crowdfunding videos from a leading online crowdfunding website, and shows that the proposed measures have explanatory power on the funding outcomes of the projects. These measures can be effectively implemented and used for large datasets. Further, researchers can apply these measures to other sets of visual information, and marketers could use the research to guide their video design and improve their video marketing effectiveness. (C) 2019 Elsevier B.V. All rights reserved.
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Unlimited vocabulary annotation of multimedia documents remains elusive despite progress solving the problem in the case of a small, fixed lexicon. Taking advantage of the repetitive nature of modern information and online media ...
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Unlimited vocabulary annotation of multimedia documents remains elusive despite progress solving the problem in the case of a small, fixed lexicon. Taking advantage of the repetitive nature of modern information and online media databases with independent annotation instances, we present an approach to automatically annotate multimedia documents that uses mining techniques to discover new annotations from similar documents and to filter existing incorrect annotations. The annotation set is not limited to words that have training data or for which models have been created. It is limited only by the words in the collective annotation vocabulary of all the database documents. A graph reinforcement method driven by a particular modality (e.g., visual) is used to determine the contribution of a similar document to the annotation target. The graph supplies possible annotations of a different modality (e.g., text) that can be mined for annotations of the target. Experiments are performed using videos crawled from YouTube. A customized precision-recall metric shows that the annotations obtained using the proposed method are superior to those originally existing for the document. These extended, filtered tags are also superior to a state-of-the-art semi-supervised technique for graph reinforcement learning on the initial user-supplied annotations.
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Video mining algorithms such as concept classification and person recognition enable fine-grained semantic search in large video archives like the historical collection of the former German Democratic Republic (GDR) of the German ...
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Video mining algorithms such as concept classification and person recognition enable fine-grained semantic search in large video archives like the historical collection of the former German Democratic Republic (GDR) of the German Broadcasting Archive (DRA). We present the project VIVA, our deep learning approaches, and the VIVA software tool, which allows users to easily acquire data to train analysis algorithms.
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This paper targets at the problem of automatic semantic indexing of news videos by presenting a video annotation and retrieval system which is able to perform automatic semantic annotation of news video archives and provide access...
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This paper targets at the problem of automatic semantic indexing of news videos by presenting a video annotation and retrieval system which is able to perform automatic semantic annotation of news video archives and provide access to the archives via these annotations. The presented system relies on the video texts as the information source and exploits several information extraction techniques on these texts to arrive at representative semantic information regarding the underlying videos. These techniques include named entity recognition, person entity extraction, coreference resolution, and semantic event extraction. Apart from the information extraction components, the proposed system also encompasses modules for news story segmentation, text extraction, and video retrieval along with a news video database to make it a full-fledged system to be employed in practical settings. The proposed system is a generic one employing a wide range of techniques to automate the semantic video indexing process and to bridge the semantic gap between what can be automatically extracted from videos and what people perceive as the video semantics. Based on the proposed system, a novel automatic semantic annotation and retrieval system is built for Turkish and evaluated on a broadcast news video collection, providing evidence for its feasibility and convenience for news videos with a satisfactory overall performance.
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