Description and retrieval of medical visual information based on language modelling

Keywords: concept-based retrieval, query expansion, language model, UMLS, MetaMap, Indri..

Our vali- dation results with the last year 2011 dataset indicates both ITML and diffusion distance to be promising choices for the ad-hoc image-based retrieval task for

For the modality classification and image retrieval tasks, our best results were obtained using mixed approaches, indicating the importance of both textual and visual features for

queries cbir without case backoff (mixed): Essie search like run (3) but form- ing the query using the captions of the top 3 images retrieved using a content- based

To generate the feature vectors at different levels of abstraction, we extract both visual concept- based feature based on a “bag of concepts” model comprising color and texture

• Content Based Image Retrieval (CBIR) systems use low-level image features to return images similar to an image used as example.. The main problem of this approach is that

For the medical image retrieval task, we distinguished gray images from color images and used different kinds of visual features to describe them. Our submission ranked second among

Linguistic Engineering, Information Retrieval, image retrieval, cross-lingual, content-based retrieval, visual, text-based retrieval, textual, relevance feedback, GIFT, KSite,