Transductive inference using multiple experts for brushwork annotation in paintings domain

Abstract

Many recent studies perform annotation of paintings based on brushwork. In these studies the brushwork is modeled indirectly as part of the annotation of high-level artistic concepts such as the artist name using low-level texture. In this paper, we develop a serial multi-expert framework for explicit annotation of paintings with brushwork classes. In the proposed framework, each individual expert implements transductive inference by exploiting both labeled and unlabelled data. To minimize the problem of noise in the feature space, the experts select appropriate features based on their relevance to the brushwork classes. The selected features are utilized to generate several models to annotate the unlabelled patterns. The experts select the best performing model based on Vapnik combined bound. The transductive annotation using multiple experts out-performs the conventional baseline method in annotating patterns with brushwork classes.