PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Jointly Optimising Relevance and Diversity in Image Retrieval
Thomas Deselaers, Tobias Gass, Philippe Dreuw and Hermann Ney
In: CIVR 2009, 8-10 Jul 2009, Island of Santorini, Greece.


In this paper we present a method to jointly optimise the relevance and the diversity of the results in image retrieval. Without considering diversity, image retrieval systems often mainly find a set of very similar results, so called near duplicates, which is often not the desired behaviour. From the user perspective, the ideal result consists of documents which are not only relevant but ideally also diverse. Most approaches addressing diversity in image or information retrieval use a two-step approach where in a first step a set of potentially relevant images is determined and in a second step these images are reranked to be diverse among the first positions. In contrast to these approaches, our method addresses the problem directly and jointly optimises the diversity and the relevance of the images in the retrieval ranking using techniques inspired by dynamic programming algorithms. We quantitatively evaluate our method on the ImageCLEF 2008 photo retrieval data and obtain results which outperform the state of the art. Additionally, we perform a qualitative evaluation on a new product search task and it is observed that the diverse results are more attractive to an average user.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Multimodal Integration
Information Retrieval & Textual Information Access
ID Code:5467
Deposited By:Thomas Deselaers
Deposited On:27 September 2009