MedIC at ImageCLEF 2006: automatic image categorization and annotation using combined visual representations

Authors:
Filip Florea;Alexandrina Rogozan;Eugen Barbu;Abdelaziz Bensrhair;Stefan Darmoni
Affiliations:
LITIS Laboratory, France and Rouen University Hospital & GCSIS, Rouen, France;LITIS Laboratory, France;LITIS Laboratory, France;LITIS Laboratory, France;LITIS Laboratory, France and Rouen University Hospital & GCSIS, Rouen, France
Venue:
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Year:
2006

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Abstract

The CISMeF group participated at the automatic annotation task of the 2006 ImageCLEF cross-language image retrieval track, employing the MedIC module. The module is designed to automatically extract annotations using image categorization. For the 2006 ImageCLEF annotation experiments we used two sets of visual representations: the first based on the PCA transformation of combined textural and statistic low-level visual features and the second oriented towards compact symbolic image descriptors extracted from the same low-level features. Only the first set of features was present in the actual competition and obtained the fourth rank, with only 1% of error more than the most accurate run of the competition. The comparison with the second representation set was conducted after the official benchmark, on the validation data set, and obtained similar results, but with significantly smaller image signatures.