Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

A Data-Driven Approach for Tag Refinement and Localization in Web Videos

Tagging of visual content is becoming more and more widespread as web-based services and social networks have popularized tagging functionalities among their users. These user-generated tags are used to ease browsing and exploration of media collections, e.g. using tag clouds, or to retrieve multimedia content. However, not all media are equally tagged by users. Using the current systems is easy to tag a single photo, and even tagging a part of a photo, like a face, has become common in sites like Flickr and Facebook. On the other hand, tagging a video sequence is more complicated and time consuming, so that users just tag the overall content of a video. In this paper we present a method for automatic video annotation that increases the number of tags originally provided by users, and localizes them temporally, associating tags to keyframes. Our approach exploits collective knowledge embedded in user-generated tags and web sources, and visual similarity of keyframes and images uploaded to social sites like YouTube and Flickr, as well as web sources like Google and Bing. Given a keyframe, our method is able to select on the fly from these visual sources the training exemplars that should be the most relevant for this test sample, and proceeds to transfer labels across similar images. Compared to existing video tagging approaches that require training classifiers for each tag, our system has few parameters, is easy to implement and can deal with an open vocabulary scenario. We demonstrate the approach on tag refinement and localization on DUT-WEBV, a large dataset of web videos, and show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU

Evaluation campaigns and TRECVid

The TREC Video Retrieval Evaluation (TRECVid) is an international benchmarking activity to encourage research in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video corpus, automatic detection of a variety of semantic and low-level video features, shot boundary detection and the detection of story boundaries in broadcast TV news. This paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether such campaigns are a good thing or a bad thing. There are arguments for and against these campaigns and we present some of them in the paper concluding that on balance they have had a very positive impact on research progress