MIRACLE-GSI at ImageCLEFphoto 2008: Different Strategies for Automatic Topic Expansion

Abstract

This paper describes the participation of MIRACLE-GSI research consortium at the ImageCLEFphoto task of ImageCLEF 2008. For this campaign, the main purpose of our experiments was to evaluate different strategies for topic expansion in a pure textual retrieval context. Two approaches were used: methods based on linguistic information such as thesauri, and statistical methods that use term frequency. First a common baseline algorithm was used in all experiments to process the document collection. Then different expansion techniques are applied. For the semantic expansion, we used WordNet to expand topic terms with related terms. The statistical method consisted of expanding the topics using Agrawal’s apriori algorithm. Relevance-feedback techniques were also used. Last, the result list is reranked using an implementation of k-Medoids clustering algorithm with the target number of clusters set to 20. 14 fully-automatic runs were finally submitted. MAP values achieved are on the average, comparing to other groups. However, results show a significant improvement in cluster precision (6% at CR10, 12% at CR20, for runs in English) when clustering is applied, thus proving to be valuable.