Experiences at ImageCLEF 2010 using CBIR and TBIR mixing information approaches
Joan Benavent, Xaro Benavent, Esther de Ves, Rubén Granados, Ana M. García-Serrano · 2010
Abstract. The main goal of this paper it is to present our experiments in ImageCLEF 2010 Campaign (Wikipedia retrieval task). This edition we present a different way of using textual and visual information based on the assumption that the textual module better captures the meaning of a topic. So that, the TBIR module works firstly and acts as a filter, and the CBIR system reorder the textual result list. The CBIR system presents three different algorithms: the automatic, the query expansion and a logistic regression relevance feedback algorithm. We have submitted nine textual and eleven mixed runs. Our best run, at the 34 th position (25 % at the first result list), is a textual run using our own implemented algorithm based on a VSM approach and TF-IDF weights (included in the IDRA tool) and all languages for annotation and for the topics. Our best mixed run (51th position is at 60 % first result list) is using the textual list and the logistic regression relevance algorithm at the CBIR module. Most of our runs are above the average of its own modality for the different measures. The new system architecture with the IDRA tool for the textual module and the logistic regression relevance algorithm for the visual module are the right track to maintain in our research lines.