通过融合书法字形状、方向和上下文特征并结合反馈技术,实现一种计算机辅助书法牌匾设计的方法.首先提取书法字特征表达书法字风格,然后通过风格一致性模型得到牌匾候选集,最后用对等反馈传递调整书法字的风格相似度.通过实验测试了不同的融合方法和反馈机制对牌匾生成的影响,结果表明融合和反馈均能提高牌匾的生成效果.

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.

为了高效地利用数字化书法作品,提出一种基于骨架相似性的书法字检索方法.首先用融合数学形态学的索引表骨架提取方法（MFITS）获取骨架,即先使用索引表对书法字进行初步细化,再用数学形态学的方法细化得到骨架.在提取检索字及候选字骨架后,通过计算和比较检索字与每个候选字骨架的相似性进行书法字检索.与基于轮廓相似度的书法字检索方法相比,文中方法所需检索时间减少了70%,而查全率和查准率基本保持不变,提高了大数据量书法字检索的效率.

Chinese calligraphy works is a valuable part of the Chinese culture heritage. More and more calligraphy works images are digitized, preserved and exhibited in digital library. Users always want to appreciate the style-similar works simultaneously. To satisfy their need, calligraphic style representation and browsing calligraphy works by its style are the most important problems to be addressed. This paper proposes calligraphic style representation which is a multinomial probability distribution over visual words, and Latent Style Model to discover the style of calligraphy works and organize the works by style. In our experiments, we evaluated various factors that influence the model, and proved the effectiveness of the style representation and the model. At last, we illustrate the Calligraphic Style Browser to organize and exhibit the resource according to the styles. © 2008 Elsevier Inc. All rights reserved.

Libraries and museums are digitizing their collections of historical culture objects to enable public access, such as historical Chinese calligraphy. These collections are only available in image format, lacking practical technology to offer the basic search service for public access. This paper proposes a quick search approach by a coarse-to-fine strategy. First, long list of calligraphy characters are pruned into shorter list by eliminating characters that have no possibility to be similar to the query. Then shape matching algorithm is employed to quantitatively measure the similarity between the query and each remainder calligraphy character. Finally, the efficiency of the algorithm is demonstrated by experiments with 12,021 images of calligraphy character.