摘要
为了高效地识别利用数字化书法作品,文章提出一种改进的Z-S书法字细化算法,结合全局特征与局部特征进行双层索引。首先,利用这种改进的Z-S算法提取单像素无毛刺的书法字图像骨架信息,然后对书法字骨架的GIST全局特征进行初步筛选排序,结合书法字图片局部特征进行二次索引排序,将两种排序结果进行加权计算,得到检索结果。所有的特征数据均使用自学习哈希算法进行二进制编码,索引的过程采用加权海明距离计算的方法。试验结果表明,该方法所需的检索时间相较于骨架相似性检索方法所需时间减少了约50%,相对于自适应书法字图像匹配与检索算法,在查全率和查准率上提升了近10%,提高了大数据量书法字检索的效率。
In order to effectively recognize and utilize digital calligraphy works,this paper proposes an improved Z-S calligraphy word refinement algorithm,which combines global features and local features for double-layer index.First of all,the improved Z-S algorithm is used to extract the skeleton information of a single pixel and burr free calligraphy image,then the GIST global features of the calligraphy skeleton are preliminarily selected and sorted,combined with the local features of the calligraphy image,the secondary index sorting is carried out,and the two sorting results are weighted to get the retrieval results.All feature data are binary coded by self-learning hash algorithm,and the process of index is calculated by weighted Hamming distance.The experimental results show that the retrieval time of this method is about 50%less than that of the skeleton similarity retrieval method.Compared with the adaptive calligraphy image matching and retrieval algorithm,the recall and precision of this method are improved by nearly 10%,and the efficiency of large amount of data calligraphy character retrieval is improved.
作者
邵荣堂
李婕
巩朋成
张正文
SHAO Rongtang;LI Jie;GONG Pengcheng;ZHANG Zhengwen(School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan 430068,China)
出处
《现代信息科技》
2020年第2期7-9,12,共4页
Modern Information Technology
关键词
细化算法
全局特征
局部特征
自学习哈希
二进制编码
加权海明距离
thinning algorithm
global feature
local feature
self-learning hash
binary encoding
weighted Hamming distance
