This paper presents our submitted experiments in the Concept anno- tation and Concept Retrieval tasks using Flickr photos at ImageCLEF 2012. This edition we applied new strategies for both the textual and the visual sub- systems included in our multimodal retrieval system. The visual subsystem has focus on extending the low-level features vector with concept features. These concept features have been calculated by means of a logistic regression model. The textual subsystem has focus on expanding the query information using ex- ternal resources. Our best concept retrieval run, a multimodal one, is at the ninth position with a MnAP of 0.0295, being the second best group of the con- test for the multimodal modality. This is also our best run in the global ordered list (where eleven textual runs are also better than it). We have adapted our mul- timodal retrieval process for the annotation task obtaining non-very good results for this first participation, with a MiAP of 0.1020.
['Joan Benavent', 'Ángel Castellanos Gonzáles', 'Xaro Benavent', 'Esther de Ves', 'Ana García-Serrano']
Visual Concept Features and Textual Expansion in a Multimodal System for Concept Annotation and Retrieval with Flickr Photos at ImageCLEF2012