A decision theoretic approach to hierarchical classifier design
Pattern Recognition
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Learning-based Approach for Annotating Large On-Line Image Collection
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
IEEE Transactions on Computers
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
New approach for hierarchical classifier training and multi-level image annotation
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Graph-based methods for the automatic annotation and retrieval of art prints
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Ontology-based annotation of paintings using transductive inference framework
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Artistic image classification: an analysis on the PRINTART database
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Hi-index | 0.00 |
Many recent studies perform annotation of paintings based on brushwork. They model the brushwork indirectly as part of annotation of high-level artistic concepts such as artist name using low-level texture features and supervised inference methods. In this paper, we develop a framework for explicit annotation of paintings with brushwork classes. Brushwork classes serve as meta-level semantic concepts for artist names, paintings styles and periods of art and facilitate the incorporation of domain-specific ontologies. In particular, we employ the serial multi-expert framework with semi-supervised clustering methods to perform the annotation of brushwork patterns. Serial combination of multiple experts facilitates step-wise refinement of decisions based on the preferences of individual experts. Each individual expert performs focused subtasks using relevant feature set, which decreases the 'curse of dimensionality' and noise in the feature space. Each expert focuses on the annotation of the currently available samples from its unlabeled pool using semi-supervised agglomerative clustering. This approach is more appropriate as compared to the traditional classification methods since each brushwork class includes a variety of patterns and cannot be represented as a single distribution in the feature space. The experts exploit the distribution of unlabelled patterns and further minimize the annotation error. The multi-expert semi-supervised framework out-performs the conventional methods in annotation of patterns with brushwork classes. This framework will further be adopted to facilitate ontology-based annotation with higher-level semantic concepts such as the artist names, painting styles and periods of art.