Hierarchical Classification of Paintings Using Face- and Brush Stroke Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
An effective method to detect and categorize digitized traditional Chinese paintings
Pattern Recognition Letters
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Stochastic modeling western paintings for effective classification
Pattern Recognition
Comparison between analog and digital neural network implementations for range-finding applications
IEEE Transactions on Neural Networks
Image retrieval based on micro-structure descriptor
Pattern Recognition
A robust content based image watermarking using local invariant histogram
Multimedia Tools and Applications
A Center-Surround Histogram for content-based image retrieval
Pattern Analysis & Applications
Models to determine parameterized ordered weighted averaging operators using optimization criteria
Information Sciences: an International Journal
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fusion of supervised and unsupervised learning for improved classification of hyperspectral images
Information Sciences: an International Journal
Empirical Mode Decomposition Analysis for Visual Stylometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
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As one of the most important cultural heritages, ink and wash paintings (IWPs) play an important role in the world of traditional Chinese arts. In comparison with western arts, the Chinese IWPs have the unique feature that the art form is primarily populated with limited number of content elements, such as stones, mountains, flowers, and animals etc. and hence most likely different art pieces share similar content, making it difficult to differentiate in terms of content alone. In this paper, we propose to extract histogram-based local feature and global feature to characterize different aspects of art styles, and such features are applied to drive neural networks to complete the classification of IWPs in terms of individual artistic descriptors. We then propose a windowed and entropy balanced fusion scheme to make integrated decisions to optimize the final classification and recognition results. Extensive evaluation via experiments is also reported, which supports that the proposed algorithm achieves good performances, outperforming the existing benchmark techniques and hence providing an excellent potential for computerized analysis and management of traditional Chinese IWPs.