Leveraging video annotations in video-based e-learning

The e-learning community has been producing and using video content for a long time, and in the last years, the advent of MOOCs greatly relied on video recordings of teacher courses. Video annotations are information pieces that can be anchored in the temporality of the video so as to sustain various processes ranging from active reading to rich media editing. In this position paper we study how video annotations can be used in an e-learning context - especially MOOCs - from the triple point of view of pedagogical processes, current technical platforms functionalities, and current challenges. Our analysis is that there is still plenty of room for leveraging video annotations in MOOCs beyond simple active reading, namely live annotation, performance annotation and annotation for assignment; and that new developments are needed to accompany this evolution.Comment: 7th International Conference on Computer Supported Education (CSEDU), Barcelone : Spain (2014

Interactive object contour extraction for shape modeling

In this paper we present a semi-automatic segmentation approach suitable for extracting object contours as a precursor to 2D shape modeling. The approach is a modified and extended version of an existing state-of-the-art approach based on the concept of a Binary Partition Tree (BPT) [1]. The resulting segmentation tool facilitates quick and easy extraction of an object’s contour via a small amount of user interaction that is easy to perform, even in complicated scenes. Illustrative segmentation results are presented and the usefulness of the approach in generating object shape models is discussed

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

ELAN as flexible annotation framework for sound and image processing detectors

Annotation of digital recordings in humanities research still is, to a largeextend, a process that is performed manually. This paper describes the firstpattern recognition based software components developed in the AVATecH projectand their integration in the annotation tool ELAN. AVATecH (AdvancingVideo/Audio Technology in Humanities Research) is a project that involves twoMax Planck Institutes (Max Planck Institute for Psycholinguistics, Nijmegen,Max Planck Institute for Social Anthropology, Halle) and two FraunhoferInstitutes (Fraunhofer-Institut für Intelligente Analyse- undInformationssysteme IAIS, Sankt Augustin, Fraunhofer Heinrich-Hertz-Institute,Berlin) and that aims to develop and implement audio and video technology forsemi-automatic annotation of heterogeneous media collections as they occur inmultimedia based research. The highly diverse nature of the digital recordingsstored in the archives of both Max Planck Institutes, poses a huge challenge tomost of the existing pattern recognition solutions and is a motivation to makesuch technology available to researchers in the humanities

Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application