Using Pseudo-Relevance Feedback to Improve Image Retrieval Results

In this paper, we propose a pseudo-relevance feedback method to deal with the photographic retrieval and medical retrieval tasks of ImageCLEF 2007. The aim of our participation to ImageCLEF is to evaluate a combination method using both english textual queries and image queries to answer to topics. The approach processes image queries and merges them with textual queries in order to improve results. We do not obtain good results using only textual information and queries. To process image queries, we used the Fire system to sort similar images using low level features, and we then used associated textual information of the top images to construct a new textual query. Results showed the interest of low level features to process image queries, as performance increased compared to textual queries processing. Finally, best results were obtained combining the results lists of textual queries processing and image queries processing with a linear function . Categories and Subject Descriptors...

• Type: Conference
• Year: 2007
• Where: CLEF
• Authors: Mouna Torjmen, Karen Pinel-Sauvagnat, Mohand Boughanem
• Keywords: CLEF 2007, Image Queries, Information Retrieval, Textual Queries, Textual Queries Processing