On the Selection of Anchors and Targets for Video Hyperlinking

A problem not well understood in video hyperlinking is what qualifies a fragment as an anchor or target. Ideally, anchors provide good starting points for navigation, and targets supplement anchors with additional details while not distracting users with irrelevant, false and redundant information. The problem is not trivial for intertwining relationship between data characteristics and user expectation. Imagine that in a large dataset, there are clusters of fragments spreading over the feature space. The nature of each cluster can be described by its size (implying popularity) and structure (implying complexity). A principle way of hyperlinking can be carried out by picking centers of clusters as anchors and from there reach out to targets within or outside of clusters with consideration of neighborhood complexity. The question is which fragments should be selected either as anchors or targets, in one way to reflect the rich content of a dataset, and meanwhile to minimize the risk of frustrating user experience. This paper provides some insights to this question from the perspective of hubness and local intrinsic dimensionality, which are two statistical properties in assessing the popularity and complexity of data space. Based these properties, two novel algorithms are proposed for low-risk automatic selection of anchors and targets.Comment: ACM International Conference on Multimedia Retrieval (ICMR), 2017. (Oral

Outil hypergraphe d'exploration multimédia et pratiques de recherche-production

International audienceFollowing the development of LIMAH, we report here the research-production dynamics in which it participates. After having specified the analysis protocol and the audiovisual methodology used, we present our first exploratory results. We will then address the issue of building the credibility of information retrieval system as well as the work of research-production seen as a sense making process.Suite au développement de l'outil LIMAH (moteur d'exploration de corpus multimédia), nous rendons compte ici des dynamiques de recherche-production qui se tissent avec celui-ci. Après avoir précisé le protocole d'observation et la méthodologie audiovisuelle utilisés nous présentons nos premiers résultats exploratoires. Nous aborderons alors la question de la construction de la crédibilité du Système de Recherche d'Information ainsi que le travail de recherche-production comme production de sens

Exploiting Multimodality in Video Hyperlinking to Improve Target Diversity

International audienceVideo hyperlinking is the process of creating links within a collection of videos to help navigation and information seeking. Starting from a given set of video segments, called anchors, a set of related segments, called targets, must be provided. In 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 cross-modal 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 autoencoders offer the best trade-off between relevance and diversity, with bimodal LDA exhibiting slightly more diverse targets at a lower precision