Combining visual features and text data for medical image retrieval using latent semantic kernels

Juan C. Caicedo, Jose G. Moreno, Edwin A. Niño, Fabio A. González
2010 Proceedings of the international conference on Multimedia information retrieval - MIR '10  
In this paper we propose an strategy to fuse visual features and unstructured-text data in a medical image retrieval system. The main goal of this work is to investigate whether the semantic information from text descriptions can be transferred to a visual similarity measure. Then, a system to search using the query-by-example paradigm is evaluated instead of a keyword-based search. We achieve this by using Latent Semantic Kernels to generate a new representation space whose coordinates define
more » ... atent concepts that merge visual patterns and textual terms. The proposed method is tested in a medical image collection from the ImageCLEFmed08 challenge. The experimental evaluation tests the system using different image queries. The results show an improvement of the visual-text fused approach with respect to only using visual information.
doi:10.1145/1743384.1743442 dblp:conf/mir/CaicedoMNG10 fatcat:jvbk72tepvczvmsjjdizqobzje