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华中科技大学软件学院,湖北,武汉,430074
Published:2008
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QIU De-hong, PAN Xin-xin, CHEN Chuan-bo. Multi-Scale Sequence Spectrum Kernel Optimization Through SDP for Signature Verification[J]. Acta Electronica Sinica, 2008, 36(S1): 44-49.
变化尺度进行签名序列的相似性描述有利于获得更准确的相似性描述结果.本文定义了签名序列的变化尺度的谱核矩阵
在多个尺度的核变换空间上进行序列的相似性描述
并利用半定规划对多尺度谱核矩阵进行优化
结合支持向量机建立起一种能够自动优化签名序列多尺度相似性描述的认证方法.该方法能够适应不同个人的签名特点
克服统一尺度下相似性描述的缺陷
提高签名序列相似性描述的准确性
在相同签名数据集上的实验结果显示该方法可以获得更高的认证准确率.
Using multi-scale to measure the similarities between signature sequences is much helpful to obtain high-qualified similarity measures.This paper puts forward a new sequence similarity kernel
called multi-scale sequence spectrum kernel
to measure signature sequence similarity based on shared occurrences of different-scale continuous subsequences.The multi-scale sequence spectrum kernel is optimized through semidefinite program
and used with support vector machine to verify signature directory sequences.The experiments on the benchmark database show the signature verification accuracy has been enhanced
as this approach could automatically optimize the similarity measures with multi-scales
depending on the signature characteristics.
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