Dynamic layout of visual summaries for scalable video

The paper brings a novel method for generating visual summaries of scalable videos. The generated summaries can dynamically adapt to requirements defined by display size, userpsilas needs or channel limitations. It utilises compressed domain features coupled with efficient contour evolution algorithm in order to generate a scale space of temporal video descriptors. The layout of the visual summary is created using an efficient graph clustering technique and a fast discrete optimisation algorithm, enabling dynamic video summarisation in real-time. The experimental results show good scalability of the dynamic layout and highly efficient generation of visual summaries

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

A scalable approach to video summarization and adaptation

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201

Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic