BUAA AUDR at ImageCLEF 2012 Medical Retrieval Task

This paper presents the participation of the BUAA AUDR group at ImageCLEF 2012 at the Medical Image classification and Retrieval task. We performed two subtasks: modality classification and ad-hoc image-based retrieval. It was our first time to select modality classification task and we concentrated on mono-modal visual-based image classifier. We used LibSVM to train the classifier, and edge histogram feature to represent images. To improve its performance, we tried to extend the training set. However, due to size of the training set and other reasons, its accuracy was even worse. For adhoc image-based retrieval, we utilized MeSH as source of query expansion and only textual information were considered. We also explored mixed approaches that combine modality predication and query expansion and our best runs ranked second among all the textual runs.