UNT at ImageCLEF 2011: Relevance Models and Salient Semantic Analysis for Image Retrieval.

This paper presents the result of the team of the University of North Texas in the ImageCLEF 2011 Wikipedia and Medical Image Retrieval tasks. For Wikipedia image retrieval we compare the two query expansion methods: relevance models and query expansion using Wikipedia and flicker as external sources. The relevance models use a classic relevance feedback mechanism for Language models as proposed by Levrenko. The external query expansion mechanism uses an unsupervised two steps method that takes advantage of Salient Semantic Analysis (SSA) using Wikipedia and estimates the "picturability" of terms using Flicker tags. Our results show that SSA and Flickr picturability can be used effectively to create very competitive runs that capture the semantic context of the original query. For Medical Image Retrieval we also use relevance models and query expansion using terms generated by MetaMap.