Arni, T., Tang, J., Sanderson, M. et al. (1 more author) (2008) Creating a test collection to evaluate diversity in image retrieval. In: Beyond Binary Relevance: Preferences, Diversity and Set-Level Judgments. SIGIR 2008 Workshop, July 24th, 2008, Singapore. ACM .
This paper describes the adaptation of an existing test collection for image retrieval to enable diversity in the results set to be measured. Previous research has shown that a more diverse set of results often satisfies the needs of more users better than standard document rankings. To enable diversity to be quantified, it is necessary to classify images relevant to a given theme to one or more sub-topics or clusters. We describe the challenges in building (as far as we are aware) the first test collection for evaluating diversity in image retrieval. This includes selecting appropriate topics, creating sub-topics, and quantifying the overall effectiveness of a retrieval system. A total of 39 topics were augmented for cluster-based relevance and we also provide an initial analysis of assessor agreement for grouping relevant images into sub-topics or clusters.
|Keywords:||Diversity, image test collection, evaluation, image retrieval, building test collection|
|Institution:||The University of Sheffield|
|Academic Units:||The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)|
|Depositing User:||Repository Officer|
|Date Deposited:||19 Nov 2008 15:36|
|Last Modified:||07 Jun 2014 03:01|