70,384 research outputs found

    Strategies for Searching Video Content with Text Queries or Video Examples

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    The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

    Interactive Food and Beverage Marketing: Targeting Children and Youth in the Digital Age

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    Looks at the practices of food and beverage industry marketers in reaching youth via digital videos, cell phones, interactive games and social networking sites. Recommends imposing governmental regulations on marketing to children and adolescents

    A Better Understanding of College Students\u27 YouTube Behaviors

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    The purpose of this research study is to get a closer look into the behavior of college students towards the video streaming website YouTube. The objective is to understand whether the benefits of publishing videos on the site are positive for business organizations. The study looks at many variables that would help companies better understand what exactly publishing a video on YouTube would do for them. These variables include gender, hours of television watched, hours of Internet used, hours spent reading and whether a video is made by a regular user or a professional company. It was found that males are more likely to use YouTube then females, despite using the Internet much less. It was also shown that there are both pros and cons for implementing user and corporate developed videos

    Learning to Hash-tag Videos with Tag2Vec

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    User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash-tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data-driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study

    Jihadi video and auto-radicalisation: evidence from an exploratory YouTube study

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    Large amounts of jihadi video content on YouTube along with the vast array of relational data that can be gathered opens up innovative avenues for exploration of the support base for political violence. This exploratory study analyses the online supporters of jihad-promoting video content on YouTube, focusing on those posting and commenting upon martyr-promoting material from Iraq. Findings suggest that a majority are under 35 years of age and resident outside the region of the Middle East and North Africa (MENA) with the largest percentage of supporters located in the United States. Evidence to support the potential for online radicalisation is presented. Findings relating to newly formed virtual relationships involving a YouTube user with no apparent prior links to jihadists are discussed

    Video matching using DC-image and local features

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    This paper presents a suggested framework for video matching based on local features extracted from the DCimage of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the realtime margin. There are also various optimisations that can be done to improve this computation complexity

    Edge analytics in the internet of things

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    High-data-rate sensors are becoming ubiquitous in the Internet of Things. GigaSight is an Internet-scale repository of crowd-sourced video content that enforces privacy preferences and access controls. The architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet

    DC-image for real time compressed video matching

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    This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity
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