Discovering calligraphy style relationships by Supervised Learning Weighted Random Walk Model

Chinese calligraphy is an important part of Chinese traditional culture. More and more calligraphy works are digitized, preserved and exhibited in digital libraries. Users may want to appreciate the style-similar works simultaneously. However, currently available services such as metadata-based brow...

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Bibliographic Details
Published in:Multimedia systems 2009-08, Vol.15 (4), p.221-242
Main Authors: Lu, Weiming, Zhuang, Yueting, Wu, Jiangqin
Format: Article
Language:English
Subjects:
Applied sciences
Computer Communication Networks
Computer Graphics
Computer Science
Computer science
control theory
systems
Cryptology
Data processing. List processing. Character string processing
Data Storage Representation
Exact sciences and technology
Image processing
Information theory
Information, signal and communications theory
Memory organisation. Data processing
Operating Systems
Regular Paper
Signal processing
Software
Telecommunications and information theory
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Summary:Chinese calligraphy is an important part of Chinese traditional culture. More and more calligraphy works are digitized, preserved and exhibited in digital libraries. Users may want to appreciate the style-similar works simultaneously. However, currently available services such as metadata-based browsing and searching can not satisfy such kind of requirement. To allow users to appreciate the style-similar works conveniently, we propose a Supervised Learning Weighted Random Walk Model to discover calligraphy style relationships. In the model, we consider the heterogeneity of both edges and nodes, and then use some preference pairs to learn the weights of different types of edges in the graph. After the weight learning, the style relationships can be discovered by random walk on the heterogeneous graphs. In order to solve the out-of-graph node problem , we pre-compute the personalized vector for each character or visual word, then utilize the Linearity Theory for vector addition to approximate the relationships between the new node and other nodes in graph. Then we demonstrate several applications which prove the effectiveness and efficiency of our proposed model and a user study for benefit verification. Finally, we explore some strategies to enhance the performance with the explicit or implicit user interaction including feedback, clickthrough data tracking.
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-008-0151-z