本文選題：筆跡鑒別　切入點(diǎn)：特征提取　出處：《華中科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：筆跡鑒別是一種重要的人體生物特征識(shí)別方法,它在公安、司法、考古、金融和電子商務(wù)等各個(gè)領(lǐng)域都有廣泛的應(yīng)用,而非特定脫機(jī)手寫筆跡鑒別是筆跡鑒別中應(yīng)用范圍最廣的分支,是目前研究的熱點(diǎn)和難點(diǎn)。本文主要研究非特定人脫機(jī)手寫筆跡鑒別的算法。本文將在圖像分類中常用的Bag of Words(BoW)方法運(yùn)用到非特定人脫機(jī)手寫筆跡鑒別中。在特征提取方面,我們對(duì)SIFT特征,NoSIFT特征,SURF特征,CNN激活特征,LBP特征進(jìn)行詳細(xì)介紹和討論對(duì)比。受Contour-Hinge等特征的啟發(fā),文中提出基于輪廓點(diǎn)的ELBP和基于輪廓點(diǎn)的ESIFT特征,實(shí)驗(yàn)證明兩種基于輪廓點(diǎn)的特征包含互補(bǔ)信息,將兩種特征融合后可以進(jìn)一步提高鑒別準(zhǔn)確率。在特征編碼層面,本文對(duì)傳統(tǒng)的硬投票(Hard Voting)方法和LLC稀疏編碼方法進(jìn)行對(duì)比分析,首次提出將一種基于局部仿射子空間編碼的方法(LASC)運(yùn)用到筆跡鑒別。這種方法考慮到每個(gè)單詞周圍鄰域空間信息,因此明顯優(yōu)于傳統(tǒng)硬投票和LLC編碼方法,當(dāng)字典空間較大時(shí),該方法不會(huì)過(guò)早的出現(xiàn)過(guò)擬合現(xiàn)象,隨著字典空間變大,鑒別準(zhǔn)確率可以進(jìn)一步提高。同時(shí),本文深入討論分析了基于GMM的FV、UBM、KLD三種編碼方法并進(jìn)行了對(duì)比實(shí)驗(yàn)分析。之后本文對(duì)比分析了基于BoW的特征表達(dá)和基于GMM的特征表達(dá)各自優(yōu)缺點(diǎn)以及各自性能。最后,本文提出一種多字典特征融合的非特定人脫機(jī)手寫筆跡鑒別方法。通過(guò)將判別性較高的特征進(jìn)行加權(quán)融合,在公開(kāi)數(shù)據(jù)集ICDAR2013和CVL數(shù)據(jù)集上取得較好的效果。
[Abstract]:Handwriting identification is an important method of human biometric identification. It is widely used in many fields, such as public security, judicial, archaeological, financial and electronic commerce. Non-specific off-line handwriting identification is the most widely used branch of handwriting identification. This paper mainly studies the algorithms of off-line handwriting identification for independent people. In this paper, the Bag of Wordsof Bow method, which is commonly used in image classification, is applied to the off-line handwriting identification of independent people. In this paper, we introduce and compare the features of SIFT feature, surf feature and SIFT active feature. Inspired by Contour-Hinge and other features, we propose ELBP based on contour point and ESIFT feature based on contour point. Experiments show that the two features based on contour points contain complementary information, and the accuracy of the two features can be further improved after the fusion of the two features. In this paper, the traditional hard voting method and LLC sparse coding method are compared and analyzed. A local affine subspace coding method based on local affine subspace coding is proposed for the first time. This method takes into account the neighborhood space information of each word, so it is superior to the traditional hard voting and LLC coding methods, when the dictionary space is large. This method does not appear the phenomenon of over-fitting prematurely. With the dictionary space increasing, the discriminant accuracy can be further improved. At the same time, In this paper, three coding methods based on GMM are discussed and compared. Then, the advantages and disadvantages of feature expression based on BoW and feature expression based on GMM are compared and analyzed. In this paper, a new method of off-line handwritten handwriting identification for independent individuals with multi-dictionary feature fusion is proposed. By weighted fusion of the higher discriminant features, better results are obtained on the open dataset ICDAR2013 and CVL datasets.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 謝紅;趙洪野;;基于卡方距離度量的改進(jìn)KNN算法[J];應(yīng)用科技;2015年01期

2 李昕;丁曉青;;基于改進(jìn)微結(jié)構(gòu)特征的筆跡鑒別[J];清華大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年04期

相關(guān)碩士學(xué)位論文 前1條

1 王東利;基于邊緣方向特征的離線筆跡鑒別[D];華東師范大學(xué);2013年

，

本文編號(hào)：1621806