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
Retrieval of Paintings using Effects Induced by Color Features
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Transductive inference using multiple experts for brushwork annotation in paintings domain
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Proceedings of the 15th international conference on Multimedia
Computational Aesthetics 2008: Categorizing art: Comparing humans and computers
Computers and Graphics
Auto-annotation of paintings using social annotations,domain ontology and transductive inference
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Learning from humanoid cartoon designs
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Perceptual and computational categories in art
Computational Aesthetics'08 Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
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Domain-specific knowledge of paintings defines a wide range of concepts for annotation and flexible retrieval of paintings. In this work, we employ the ontology of artistic concepts that includes visual (or atomic) concepts at the intermediate level and high-level concepts at the application level. Visual-level concepts include artistic color and brushwork concepts that serve as cues for annotating high-level concepts such as the art periods for paintings. To assign artistic color concepts, we utilize inductive inference method based on probabilistic SVM classification. For brushwork annotation, we employ previously developed transductive inference framework that utilizes multi-expert approach, where individual experts implement transductive inference by exploiting both labeled and unlabelled data. In this paper, we combine the color and brushwork concepts with low-level features and utilize a modification of the transductive inference framework to annotate art period concepts to the paintings collection. Our experiments on annotating art period concepts demonstrate that: a) the use of visual-level concepts significantly improves the accuracy as compared to using low-level features only; and b) the proposed framework out-performs the conventional baseline method.