Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximation algorithms for the metric labeling problem via a new linear programming formulation
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
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
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Ontology-based annotation of paintings using transductive inference framework
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning ontology rules for semantic video annotation
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
Affective Space Exploration for Impressionism Paintings
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Faceted search and retrieval based on semantically annotated product family ontology
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
Computational Aesthetics 2008: Categorizing art: Comparing humans and computers
Computers and Graphics
Multimedia Tools and Applications
Information Processing and Management: an International Journal
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
A tactile-thermal display for haptic exploration of virtual paintings
The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility
Perceptual and computational categories in art
Computational Aesthetics'08 Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Enhancing semantic features with compositional analysis for scene recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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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 color and brushwork concepts are widely used by art historians to analyze paintings and serve as cues for annotating high-level concepts such as the artist names, painting styles and art periods for paintings. In this research we combine the color and brushwork concepts with low-level features and utilize the transductive inference framework to annotate high-level concepts to the image blocks. In order to resolve conflicting assignments of high-level concepts, we further employ the ontology-based concept disambiguation method and generate image-level annotations. This method performs global optimization of the block-level annotations using the linear constraints extracted from domain knowledge. Our experiments on annotating high-level 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 transductive inference framework out-performs the conventional baseline methods and c) the proposed ontology-based disambiguation method generates superior results for several annotation scenarios.