Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

Exploration of applying a theory-based user classification model to inform personalised content-based image retrieval system design

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published at http://dl.acm.org/citation.cfm?id=2903636To better understand users and create more personalised search experiences, a number of user models have been developed, usually based on different theories or empirical data study. After developing the user models, it is important to effectively utilise them in the design, development and evaluation of search systems to improve users’ overall search experiences. However there is a lack of research has been done on the utilisation of the user models especially theory-based models, because of the challenges on the utilization methodologies when applying the model to different search systems. This paper explores and states how to apply an Information Foraging Theory (IFT) based user classification model called ISE to effectively identify user’s search characteristics and create user groups, based on an empirically-driven methodology for content-based image retrieval (CBIR) systems and how the preferences of different user types inform the personalized design of the CBIR systems

Formal models, usability and related work in IR (editorial for special edition)

The Glasgow IR group has carried out both theoretical and empirical work, aimed at giving end users efficient and effective access to large collections of multimedia data

Simulated evaluation of faceted browsing based on feature selection

In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness