Using medGIFT and easyIR for the ImageCLEF 2005 Evaluation Tasks

The goal of the CLEF medical retrieval task is to advance the performance of multimedia objects retrieval in the medical domain combining techniques from Information Retrieval

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

In this year, we adopted a more principled and efficient phrase-based retrieval model, which was implemented based on Indri search engine [1] and their structured query language..

Cross-modal Information Retrieval, Image Modality Classification, Medical Image Retrieval, Wikipedia Retrieval, Fisher Vector, Lexical Entailment, Query Expansion..

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

So, we consider using phrase (subphrase) instead of concept (CUI) to represent document, and phrases, subphrases and individual words are all used as index terms3. The subphrases of

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

3.2.8 IRMA: RWTH Aachen University, Medical Informatics, Aachen, Germany The IRMA group from the RWTH Aachen University Hospital 21 , in Aachen Germany submitted three baseline