This paper presents the algorithms and results of the "idiap" team participation to the ImageCLEFmed annotation task in 2008. On the basis of our successful experience in 2007 we decided to integrate two different local structural and textural descriptors. Cues are combined through concatenation of feature vectors and through the Multi-Cue Kernel. The challenge this year was to annotate images coming mainly from classes with only few training examples. We tackled the problem on two fronts: (1) we introduced a further integration strategy using SVM as an opinion maker; (2) we enriched the poorly populated classes adding virtual examples. We submitted several runs considering different combinations of the proposed techniques. The run jointly using the feature concatenation, the confidence-based opinion fusion and the virtual examples ranked first among all submissions. © 2009 Springer Berlin Heidelberg.
An SVM confidence-based approach to medical image annotation / Tommasi, Tatiana; Orabona, Francesco; Caputo, Barbara. - ELETTRONICO. - 5706:(2009), pp. 696-703. (Intervento presentato al convegno 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008 tenutosi a Aarhus; Denmark nel 2008) [10.1007/978-3-642-04447-2_88].
An SVM confidence-based approach to medical image annotation
TOMMASI, TATIANA;CAPUTO, BARBARA
2009
Abstract
This paper presents the algorithms and results of the "idiap" team participation to the ImageCLEFmed annotation task in 2008. On the basis of our successful experience in 2007 we decided to integrate two different local structural and textural descriptors. Cues are combined through concatenation of feature vectors and through the Multi-Cue Kernel. The challenge this year was to annotate images coming mainly from classes with only few training examples. We tackled the problem on two fronts: (1) we introduced a further integration strategy using SVM as an opinion maker; (2) we enriched the poorly populated classes adding virtual examples. We submitted several runs considering different combinations of the proposed techniques. The run jointly using the feature concatenation, the confidence-based opinion fusion and the virtual examples ranked first among all submissions. © 2009 Springer Berlin Heidelberg.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.