Evaluating the performance in automatic image annotation: Example case by adaptive fusion of global image features

Authors:
Ville Viitaniemi;Jorma Laaksonen
Affiliations:
Adaptive Informatics Research Centre, Helsinki University of Technology (TKK), P.O. Box 5400, FI-02015 TKK, Espoo, Finland;Adaptive Informatics Research Centre, Helsinki University of Technology (TKK), P.O. Box 5400, FI-02015 TKK, Espoo, Finland
Venue:
Image Communication
Year:
2007

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Abstract

In this work we consider two traditional metrics for evaluating performance in automatic image annotation, the normalised score (NS) and the precision/recall (PR) statistics, particularly in connection with a de facto standard 5000 Corel image benchmark annotation task. We also motivate and describe another performance measure, de-symmetrised termwise mutual information (DTMI), as a principled compromise between the two traditional extremes. In addition to discussing the measures theoretically, we correlate them experimentally for a family of annotation system configurations derived from the PicSOM image content analysis framework. Looking at the obtained performance figures, we notice that such kind of a system, based on adaptive fusion of numerous global image features, clearly outperforms the considered methods in literature.