Using Ontology Dimensions and Negative Expansion to solve Precise Queries in the ImageCLEF Medical Task

The main aspects that distinguishes this challenge in comparison to other evaluations or competitions related to search- ing in handwritten documents are: multiple word queries,

One approach to cope with these shortcomings is to explicitly map the document content to entities of a formal knowledge base, and to exploit this information by taking into

The results presented in Table 1 show globally that query expansion using only terms’ synonyms from MeSH improves the IR performance in our experiments (Run 6).. Although

In the following sections, we describe the textual and visual features that comprise our image and article representations (Sections 2–3) and our methods for the modality

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

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

In 2009 medical ImageCLEF, we evaluated a new document expansion strategy, by combining the both strategies applied in 2008: to expand with MeSH descriptors automatically

The parameters for this configuration are the number of sentences that form a passage, the weighting model to use, the type of expansion, the number of documents/passages on which