A decision theoretic approach to hierarchical classifier design
Pattern Recognition
Content-based retrieval of segmented images
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Roughness Analysis and Synthesis via Extended Self-Similar (ESS) Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Proceedings of the 15th international conference on Multimedia
Ontology-based annotation of paintings using transductive inference framework
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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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.