發(fā)布時(shí)間：2018-12-11 16:40

【摘要】：為了改善計(jì)算機(jī)打印文件的自動(dòng)鑒別性能,提出了一種基于Gabor特征和Fisher判別準(zhǔn)則核字典學(xué)習(xí)的激光打印文件鑒別算法。首先提取字符圖像的Gabor幅值特征,同時(shí)將Gabor數(shù)據(jù)特征映射到高維核空間進(jìn)行主成分分析;再將降維的特征作為初始字典,進(jìn)行基于Fisher判別準(zhǔn)則的字典學(xué)習(xí);最后利用稀疏表示分類方法進(jìn)行鑒別。在自建數(shù)據(jù)庫上的實(shí)驗(yàn)結(jié)果表明Gabor特征在打印文件機(jī)源認(rèn)證中是一種有效的鑒別特征,實(shí)驗(yàn)結(jié)果還驗(yàn)證了Gabor特征和Fisher判別準(zhǔn)則核字典學(xué)習(xí)算法的有效性,打印文件源打印機(jī)正確鑒別率可達(dá)95.79%。
[Abstract]:In order to improve the automatic discrimination performance of computer printed files, a laser print file identification algorithm based on Gabor features and Fisher discriminant criterion kernel dictionary learning is proposed. Firstly, the Gabor amplitude feature of character image is extracted, at the same time, the feature of Gabor data is mapped to high dimensional kernel space for principal component analysis, then the reduced dimension feature is taken as the initial dictionary, and the dictionary learning based on Fisher discriminant criterion is carried out. Finally, the sparse representation classification method is used to identify. The experimental results on the self-built database show that the Gabor feature is an effective discriminant feature in the print file source authentication. The experimental results also verify the validity of the Gabor feature and the Fisher discriminant criterion kernel dictionary learning algorithm. The correct identification rate of print file source printer can reach 95.79.
【作者單位】： 湖北工程學(xué)院物理與電子信息工程學(xué)院;武漢大學(xué)電子信息學(xué)院;
【基金】：湖北省教育廳項(xiàng)目(B2015033) 湖北工程學(xué)院科研項(xiàng)目(201511) 湖北省大學(xué)生創(chuàng)新訓(xùn)練項(xiàng)目(201610528004)資助
【分類號(hào)】：TP301.6;TP334.8

【相似文獻(xiàn)】

相關(guān)期刊論文 前1條

1 ;Fast parallel algorithms for discrete Gabor expansion and transform based on multirate filtering[J];Science China(Information Sciences);2012年02期

相關(guān)博士學(xué)位論文 前1條

1 林晨;離散Gabor變換與展開快速并行算法分析與實(shí)現(xiàn)[D];安徽大學(xué);2015年

，

本文編號(hào)：2372882