ABSTRACT
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.
- Arnheim. Art and visual perception: A psychology of the creative eye, University of California Press, 1954.]]Google Scholar
- Art & Architecture Thesaurus. Getty Research Institute,2000.]]Google Scholar
- Canny J. A Computational Approach to Edge Detection, IEEE PAMI (8)- 6, 1986.]] Google ScholarDigital Library
- Chua T.-S., Lim S.-K., Pung H.-K.. "Content-based retrieval of segmented images". ACM MM, 211 -- 218, 1994.]] Google ScholarDigital Library
- Feng H., Chua T.-S. A Learning-based Approach for Annotating Large On-Line Image Collection. The International Multi-Media Modelling, 249-256, 2004.]] Google ScholarDigital Library
- Friedman J. H., An overview of predictive learning and function approximation, From Statistics to Neural Networks, Springer Verlag, NATO/ASI, 1-61,1994.]]Google Scholar
- Gluskman H. A. Multicategory classification of patterns represented by high-order vectors of multilevel measurements. IEEE Tran son Computers (20), 1593--1598.]] Google ScholarDigital Library
- Herik, H.J. van den, Postma, E.O. Discovering the Visual Signature of Painters. In Future Directions for Intelligent Systems and Information Sciences, 129-147, 2000.]]Google ScholarCross Ref
- Kaplan L. M. and Kuo C.-C. J., "Texture roughness analysis and synthesis via extended self-similar (ESS) model," IEEE Trans. Pattern Anal. Machine Intell (17), 1043--1056, 1995.]] Google ScholarDigital Library
- Kittler J, Hatef M. Improving recognition rates by classifier combination. 5th Int Workshop on Frontiers of Handwriting Recognition, 81--102, 1996.]]Google Scholar
- Klein, D., Kamvar, S. D., & Manning, C. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. Proceedings of ICML, 307--314, 2002.]] Google ScholarDigital Library
- Li J., Wang J. Z. Studying Digital Imagery of Ancient Paintings by Mixtures of Stochastic Models, IEEE Transactions on Image Processing, vol. 13 (3), 2004.]]Google ScholarDigital Library
- Mallat S. A theory for multi-resolution signal decomposition: the wavelet representation, IEEE PAMI (11), 674-693, 1989.]] Google ScholarDigital Library
- Mandelbrot B. B, The Fractal Geometry of Nature. San Francisco, CA: Freeman, 1982.]]Google Scholar
- Manjunath B. S., Ma W. Y., "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Machine Intell (18), 837--842, 1996.]] Google ScholarDigital Library
- Marchenko Y., Chua T.-S., Aristarkhova I., Jain R. Representation and Retrieval of Paintings based on Art History Concepts. IEEE Int'l Conf. on Multimedia and Expo (ICME), 2004.]]Google Scholar
- Marchenko Y., Chua T.-S., Aristarkhova I., Analysis of paintings using Color Concepts. IEEE ICME, 2005.]]Google Scholar
- Melzer, T., Kammerer, P., Zolda E. Stroke detection of Brush Strokes in Portrait Miniatures using Semi-Parametric and a Model-Based Approach. In Proc. of 14th ICPR, 1998.]] Google ScholarDigital Library
- Pudil P, Novovicova J, Blaha S. Multistage pattern recognition with reject option,1th IAPR ICPR, 92--95, 1992.]]Google Scholar
- Rahman A. F. R, Fairhurst M. C: Serial Combination of Multiple Experts: A Unified Evaluation. Pattern Anal. Appl. 2(4), 292--311, 1999.]]Google ScholarCross Ref
- Schueermann J, Doster W. A decision theoretic approach to hierarchical classifier design. Pattern Recognition; 17(3), 359--369, 1983.]] Google ScholarDigital Library
- Tung A., Han J., Lakshmanan L., Ng R., Constraint Based Clustering in Large Databases Proc. Int'l Conf. Database Theory Conf., pp. 405--419, 2001.]] Google ScholarDigital Library
- Teague, M.R. Image Analysis via the General Theory of Moments, Journal of the Optical Society of America, 70 (8), 920--930.]]Google ScholarCross Ref
- Vapnik, V. Estimation of Dependences Based on Empirical Data. Springer Verlag, New York, 1982.]] Google ScholarDigital Library
- Zhang D. S.and Lu G. Content-Based Shape Retrieval Using Different Shape Descriptors: A Comparative Study, IEEE ICME, 2001.]]Google Scholar
Index Terms
- Semi-supervised annotation of brushwork in paintings domain using serial combinations of multiple experts
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