Visual Concept Features and Textual Expansion in a Multimodal System for Concept Annotation and Retrieval with Flickr Photos at ImageCLEF2012

This paper presents our submitted experiments in the Concept annotation and Concept Retrieval tasks using Flickr photos at ImageCLEF 2012. This edition we applied new strategies for both the textual and the visual subsystems 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 external 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 contest 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 multimodal retrieval process for the annotation task obtaining non-very good results for this first participation, with a MiAP of 0.1020.