Deliverable D7.5 LinkedTV Dissemination and Standardisation Report v2

This deliverable presents the LinkedTV dissemination and standardisation report for the project period of months 19 to 30 (April 2013 to March 2014)

Real Time GAZED: Online Shot Selection and Editing of Virtual Cameras from Wide-Angle Monocular Video Recordings

Eliminating time-consuming post-production processes and delivering high-quality videos in today's fast-paced digital landscape are the key advantages of real-time approaches. To address these needs, we present Real Time GAZED: a real-time adaptation of the GAZED framework integrated with CineFilter, a novel real-time camera trajectory stabilization approach. It enables users to create professionally edited videos in real-time. Comparative evaluations against baseline methods, including the non-real-time GAZED, demonstrate that Real Time GAZED achieves similar editing results, ensuring high-quality video output. Furthermore, a user study confirms the aesthetic quality of the video edits produced by the Real Time GAZED approach. With these advancements in real-time camera trajectory optimization and video editing presented, the demand for immediate and dynamic content creation in industries such as live broadcasting, sports coverage, news reporting, and social media content creation can be met more efficiently

Deliverable D9.3 Final Project Report

This document comprises the final report of LinkedTV. It includes a publishable summary, a plan for use and dissemination of foreground and a report covering the wider societal implications of the project in the form of a questionnaire

USING SUM MATCH KERNEL WITH BALANCED LABEL TREE FOR LARGE-SCALE IMAGE CLASSIFICATION

Large-scale image classification is a fundamental problem in computer vision due to many real applications in various domains. A label tree-based classification is one of effective approaches for reducing the testing complexity with a large number of class labels. However, how to build a label tree structure with cost efficiency and high accuracy classification is a challenge. The popular building tree method is to apply a clustering algorithm to a similarity matrix which is obtained by training and evaluating one-versus-all classifiers on validation set. So, this method quickly become impracticable because the cost of training OvA classifiers is too high for large-scale classification problem. In this paper, we introduce a new method to obtain a similarity matrix without using one-versus-all classifiers. To measure the similarity among classes, we used the sum-match kernel that is able to be calculated simply basing on the explicit feature map. Furthermore, to gain computational efficiency in classification, we also propose an algorithm for learning balanced label tree by balancing a number of class labels in each node. The experimental results on standard benchmark datasets ImageNet-1K, SUN-397 and Caltech-256 show that the performance of the proposed method outperforms significantly other methods

On Providing Cloud-awareness to Client's DASH Application by Using DASH over HTTP/2, Journal of Telecommunications and Information Technology, 2015, nr 4

Mobile Cloud Networks group together mobile users and clouds containing content servers. Hence, they are an ideal framework for media content delivery. Streams witching adaptive video players cope well with some limitations of Mobile Cloud Networks as low bandwidth and bandwidth variability in access network. Nonetheless, other limitations, as cloud congestion, are difficult to be managed by the video players. This paper presents a system for discovering fault situations at the cloud (e.g., cloud congestion) and notifying to the video player, which will take appropriate actions for saving the quality of media transmission. In proposed implementation the video application is DASH-capable and adaptation action may be both stream rate adaptation and content server adaptation. The communication between client and server uses \bidirectional" communication feature of HTTP/2 thanks to the new deployed modules running DASH over HTTP/2 in both client's and server's applications

JTIT

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