Abstract. This paper is intended to help clarifying what multimedia analytics encompasses by studying users expectations. As a showcase, we focus on the very specific family of applications doing search and navigation of broadcast and social news content. This paper is first describing what professional practitioners working with news currently do. Thanks to extensive conversations with media professionals, mockup interfaces and a human-centered design methodology, we analyze the perceived usefulness of a number of functionalities leveraging existing or upcoming technologies. This analysis helps (i) determining research directions for the technology underpinning the very recent field of (multi)media analytics and (ii) understanding how multimedia analytics should be defined. In particular, dependency to the domain is discussed: are multimedia analytics tasks domain-specific or can we find general definitions?

Abstract. Video hyperlinking is the process of creating links within a collection of videos. Starting from a given set of video segments, called anchors, a set of related segments, called targets, must be provided. In the past years, a number of content-based approaches have been proposed with good results obtained by searching for target segments that are very similar to the anchor in terms of content and information. Unfortunately, relevance has been obtained to the expense of diversity. In this paper, we study multimodal approaches and their ability to provide a set of diverse yet relevant targets. We compare two recently introduced crossmodal approaches, namely, deep auto-encoders and bimodal LDA, and experimentally show that both provide significantly more diverse targets than a state-of-the-art baseline. Bimodal auto-encoders offer the best trade-off between relevance and diversity, with bimodal LDA exhibiting slightly more diverse targets at a lower precision.

International audienceWe investigate video hyperlinking based on speech transcripts , leveraging a hierarchical topical structure to address two essential aspects of hyperlinking, namely, serendipity control and link justification. We propose and compare different approaches exploiting a hierarchy of topic models as an intermediate representation to compare the transcripts of video segments. These hierarchical representations offer a basis to characterize the hyperlinks, thanks to the knowledge of the topics who contributed to the creation of the links, and to control serendipity by choosing to give more weights to either general or specific topics. Experiments are performed on BBC videos from the Search and Hyperlinking task at MediaEval. Link precisions similar to those of direct text comparison are achieved however exhibiting different targets along with a potential control of serendipity

As the amount of news information available online grows, media professionals are in need of advanced tools to explore the information surrounding speci c events before writing their own piece of news, e.g., adding context and insight. While many tools exist to extract information from large datasets, they do not o er an easy way to gain insight from a news collection by browsing, going from article to article and viewing unaltered original content. Such browsing tools require the creation of rich underlying structures such as graph representations. ese representations can be further enhanced by typing links that connect nodes, in order to inform the user on the nature of their relation. In this article, we introduce an e cient way to generate links between news items in order to obtain an easily navigable graph, and enrich this graph by automatically typing created links. User evaluations are conducted on real world data in order to assess for the interest of both the graph representation and link typing in a press reviewing task, showing a signi cant improvement compared to classical search engines.

Faced with ever-growing news archives, media professionals are in need of advanced tools to explore the information surrounding specific events. This problem is most commonly answered by browsing news datasets, going from article to article and viewing unaltered original content. In this article, we introduce an efficient way to generate links between news items, allowing such browsing through an easily explorable graph, and enrich this graph by automatically typing links in order to inform the user on the nature of the relation between two news pieces. User evaluations are conducted on real world data with journalists in order to assess for the interest of both the graph representation and link typing in a press reviewing task, showing the system to be of significant help for their work.