Images are commonly used on a daily basis for research, information and entertainment. The introduction of digital cameras and especially the incorporation of cameras in mobile phones make people able to snap photos almost everywhere at any time since their mobile phone is almost always brought with them. The fast evolution in hardware enables users to store large image collection without high ...

This year, XRCE participated in three main tasks of ImageCLEF 2010. The Visual Concept Detection and Annotation Task is presented in a separate paper. In this working note, we rather focus on our participation in the Wikipedia Retrieval Task and in two sub-tasks of the Medical Retrieval Task (Image Modality Classification and Ad-hoc Image Retrieval). We investigated mono-modal (textual and visu...

The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients’ cases in electronic health records. This paper presents ways to move beyond conventional text-based searching of th...

This article describes the participation of the Image and Text Integration (ITI) group from the United States National Library of Medicine (NLM) in the ImageCLEF 2009 medical retrieval track. Our methods encompass a variety of techniques relating to document summarization and textand content-based image retrieval. Our text-based approach utilizes the Unified Medical Language System (UMLS) synon...

Combining low-level features that represent the content of medical images with high level features that are saved with images would allow the expansion of text queries submitted to Content Based Image Retrieval (CBIR) systems. Expanding these text queries would allow CBIR systems to respond more effectively to specific queries when retrieving medical images. We hypothesized that adding an autom...

Content-based image retrieval (CBIR) algorithms have been seen as a promising access method for digital photo collections, sooner or later replacing the traditional text-based methods. Unfortunately, we have very little evidence of the usefulness of these algorithms in real user needs and contexts. One problem is that appropriately designed test collections are not available even for the basic ...

This research explores the interaction of linguistic and photographic information in an integrated text/image database. By utilizing linguistic descriptions of a picture (speech and text input) coordinated with pointing references to the picture, we extract information useful in two aspects: image interpretation and image retrieval. In the image interpretation phase, objects and regions mention...

We describe and analyze our participation in the WikipediaMM task at ImageCLEF 2010. Our approach is based on text-based image retrieval using information retrieval techniques on the metadata documents of the images. We submitted two English monolingual runs and one multilingual run. The monolingual runs used the query to retrieve the metadata document with the query and document in the same la...

When short queries and short image annotations are used in text-based cross-language image retrieval, small changes in word usage due to translation errors may decrease the retrieval performance because of an increase in lexical mismatches. In the ImageCLEF2005 adhoc task, we investigated the use of learned word association models that represent how pairs of words are related to absorb such mis...

Abstract With the popularity of Internet and rapid growth multimodal data, retrieval has gradually become a hot area research. As one important branches retrieval, image-text aims to design model learn align two modal image text, in order build bridge semantic association between heterogeneous so as achieve unified alignment retrieval. The current mainstream cross-modal approaches have made goo...