1,053 research outputs found

    Access to recorded interviews: A research agenda

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    Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

    Multimedia search without visual analysis: the value of linguistic and contextual information

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    This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

    Large-scale image collection cleansing, summarization and exploration

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    A perennially interesting topic in the research field of large scale image collection organization is how to effectively and efficiently conduct the tasks of image cleansing, summarization and exploration. The primary objective of such an image organization system is to enhance user exploration experience with redundancy removal and summarization operations on large-scale image collection. An ideal system is to discover and utilize the visual correlation among the images, to reduce the redundancy in large-scale image collection, to organize and visualize the structure of large-scale image collection, and to facilitate exploration and knowledge discovery. In this dissertation, a novel system is developed for exploiting and navigating large-scale image collection. Our system consists of the following key components: (a) junk image filtering by incorporating bilingual search results; (b) near duplicate image detection by using a coarse-to-fine framework; (c) concept network generation and visualization; (d) image collection summarization via dictionary learning for sparse representation; and (e) a multimedia practice of graffiti image retrieval and exploration. For junk image filtering, bilingual image search results, which are adopted for the same keyword-based query, are integrated to automatically identify the clusters for the junk images and the clusters for the relevant images. Within relevant image clusters, the results are further refined by removing the duplications under a coarse-to-fine structure. The duplicate pairs are detected with both global feature (partition based color histogram) and local feature (CPAM and SIFT Bag-of-Word model). The duplications are detected and removed from the data collection to facilitate further exploration and visual correlation analysis. After junk image filtering and duplication removal, the visual concepts are further organized and visualized by the proposed concept network. An automatic algorithm is developed to generate such visual concept network which characterizes the visual correlation between image concept pairs. Multiple kernels are combined and a kernel canonical correlation analysis algorithm is used to characterize the diverse visual similarity contexts between the image concepts. The FishEye visualization technique is implemented to facilitate the navigation of image concepts through our image concept network. To better assist the exploration of large scale data collection, we design an efficient summarization algorithm to extract representative examplars. For this collection summarization task, a sparse dictionary (a small set of the most representative images) is learned to represent all the images in the given set, e.g., such sparse dictionary is treated as the summary for the given image set. The simulated annealing algorithm is adopted to learn such sparse dictionary (image summary) by minimizing an explicit optimization function. In order to handle large scale image collection, we have evaluated both the accuracy performance of the proposed algorithms and their computation efficiency. For each of the above tasks, we have conducted experiments on multiple public available image collections, such as ImageNet, NUS-WIDE, LabelMe, etc. We have observed very promising results compared to existing frameworks. The computation performance is also satisfiable for large-scale image collection applications. The original intention to design such a large-scale image collection exploration and organization system is to better service the tasks of information retrieval and knowledge discovery. For this purpose, we utilize the proposed system to a graffiti retrieval and exploration application and receive positive feedback

    Automatic Synchronization of Multi-User Photo Galleries

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    In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

    An Overview of Video Shot Clustering and Summarization Techniques for Mobile Applications

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    The problem of content characterization of video programmes is of great interest because video appeals to large audiences and its efficient distribution over various networks should contribute to widespread usage of multimedia services. In this paper we analyze several techniques proposed in literature for content characterization of video programmes, including movies and sports, that could be helpful for mobile media consumption. In particular we focus our analysis on shot clustering methods and effective video summarization techniques since, in the current video analysis scenario, they facilitate the access to the content and help in quick understanding of the associated semantics. First we consider the shot clustering techniques based on low-level features, using visual, audio and motion information, even combined in a multi-modal fashion. Then we concentrate on summarization techniques, such as static storyboards, dynamic video skimming and the extraction of sport highlights. Discussed summarization methods can be employed in the development of tools that would be greatly useful to most mobile users: in fact these algorithms automatically shorten the original video while preserving most events by highlighting only the important content. The effectiveness of each approach has been analyzed, showing that it mainly depends on the kind of video programme it relates to, and the type of summary or highlights we are focusing on

    CONTENT BASED RETRIEVAL OF LECTURE VIDEO REPOSITORY: LITERATURE REVIEW

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    Multimedia has a significant role in communicating the information and a large amount of multimedia repositories make the browsing, retrieval and delivery of video contents. For higher education, using video as a tool for learning and teaching through multimedia application is a considerable promise. Many universities adopt educational systems where the teacher lecture is video recorded and the video lecture is made available to students with minimum post-processing effort. Since each video may cover many subjects, it is critical for an e-Learning environment to have content-based video searching capabilities to meet diverse individual learning needs. The present paper reviewed 120+ core research article on the content based retrieval of the lecture video repositories hosted on cloud by government academic and research organization of India

    Meetings and Meeting Modeling in Smart Environments

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    In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear
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