TELECOMParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement

News- REEL Multimedia tasks participants to predict items’ popularity based on text snippets and image features.. The remainder of this paper is structured as follows: Section

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

For the topics, we selected the English title as the query; for the metadata collection, the text including the image name, description, comment and caption was selected as the query

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

For this year’s Image CLEF Photo Retrieval task, we have prepared 5 submission runs to help us assess the effectiveness of 1) image content-based retrieval, and 2) text-

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

Thus, the system is built up from three main different components: Text-based retrieval module, Image-content based retrieval module and the Merging module that is in charge of

The keystone of our strategy for the previous medical ImageCLEF was to enrich documents and queries with Medical Subject Headings (MeSH) terms extracted from them, in order