MA2VICMR – Mejorando el Acceso, el Análisis y la Visibilidad de la Información y los Contenidos Multilingües y Multimedia en Red para la Comunidad de Madrid

Principal investigator: Abraham Duarte Funding entities: Comunidad de Madrid and European Social Fund (S2009/TIC-1542) Duration: 01/01/2010 - 31/12/2013


Multimedia information access systems that work on image collections usually have access to two types of data: the textual descriptors and the visual content of the images. Traditionally, these systems have approached either the problem of image retrieval by analyzing the associated textual information (TextBased Information Retrieval, TBIR) or by analyzing the visual content (ContentBased Information Retrieval, CBIR). Until a few years ago, mixed approaches did not provide any advantage to the results, besides being rather inefficient.

On the one hand, researchers from NLP&IRUNED and the Vision Team group at the University of Valencia coordinated their previous experience in textual and image content-based retrieval. The result of this collaboration has been an approach that not only takes advantage of the synergy between visual aspects and textual annotations together, but also provides an efficient computational method for the search of annotated images in large collections, from a multimedia query, either text and one or several images. This work has generated, besides participations in competitions such as ImageCLEF and MediEval, several publications in conference proceedings, an article in the IEEE Transactions on Multimedia Journal and a PhD thesis in the NLP&IRUNED group entitled Late Semantic Multimedia Fusion Applied to Multimedia Information Retrieval.

On the other hand, another mixed team formed by members of NLP&IRUNED and GAVABURJC have integrated previous technologies to build a hybrid image search system. The proposal, which combined content features and rich text analysis with linguistic resources such as WordNet, participated in two editions of the ImageCLEF Photo Annotation Task competition.

Isaac Lozano-Osorio
Isaac Lozano-Osorio
Estudiante de Doctorado en Inteligencia Artificial

Isaac Lozano se graduó en el Doble grado de Ingeniería Informática e Ingeniería de Computadores por la Universidad Rey Juan Carlos.Al finalizar el doble grado, fue galardonado con el premio al Mejor Proyecto Fin de Carrera. Posteriormente, realizó un Máster en Investigación en Inteligencia Artificial (UIMP). Actualmente realiza su tesis doctoral en la Universidad Rey Juan Carlos, dirigida por los profesores Abraham Duarte y Jesús Sánchez-Oro Sus principales intereses de investigación se centran en la interfaz entre las Ciencias de la Computación, la Inteligencia Artificial y la Investigación Operativa. La mayoría de sus publicaciones tratan sobre el desarrollo de procedimientos metaheurísticos para problemas de optimización modelados por grafos.