Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 13th annual ACM international conference on Multimedia
Enriching a Thesaurus to Improve Retrieval of Audiovisual Documents
SAMT '08 Proceedings of the 3rd International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
Thesaurus enrichment for query expansion in audiovisual archives
Multimedia Tools and Applications
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Statistical learning methods are commonly applied in content-based video and image retrieval. Such methods require a large number of examples which are usually obtained through a manual annotation process, that is human raters review images and assign semantic concept labels. The human judgement, however, cannot be regarded as the ultimate truth because of its subjectiveness and the likelihood of human error. We can address these issues by using multiple judgements per example, but evaluating and resolving disagreement between raters is problematic. Moreover, the nature of rater disagreement and how to minimise it are not yet well explored. In this paper we present results of a user study that was specifically designed to investigate human judgement of digital imagery. We discuss the influence of factors such as size and type of semantic vocabulary on inter-rater agreement. We demonstrate the application of latent class analysis for combining multiple judgements. Known from applications in the medical and social sciences, this statistic allows robust, quantitative evaluation of multiple judgements per subject. We believe it is well suited for application during the evaluation and modelling phase in semantic image and video retrieval.