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Evaluating Performance of Automatic Image Annotation: Example Case by Fusing Global Image Features

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2 Author(s)
Viitaniemi, V. ; Helsinki Univ. of Technol, Helsinki ; Laaksonen, J.

In this paper we consider two traditional metrics for evaluating the 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 a third 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 the fusion of numerous global image features clearly outperforms the considered methods in the literature.

Published in:

Content-Based Multimedia Indexing, 2007. CBMI '07. International Workshop on

Date of Conference:

25-27 June 2007