多視圖典型相關(guān)分析的理論研究和應(yīng)用
發(fā)布時(shí)間：2018-07-29 13:00
【摘要】：多視圖數(shù)據(jù)在實(shí)際應(yīng)用中普遍存在,針對(duì)多視圖數(shù)據(jù)的特征抽取也逐漸成為模式識(shí)別領(lǐng)域中的研究熱點(diǎn)和關(guān)鍵問題。多視圖典型相關(guān)分析作為一種非常重要的多視圖特征抽取方法,在國(guó)內(nèi)外已經(jīng)受到廣泛關(guān)注。然而實(shí)際采集的數(shù)據(jù)通常是復(fù)雜的非線性數(shù)據(jù),多視圖典型相關(guān)分析難以有效地處理這種實(shí)際數(shù)據(jù),這會(huì)很大程度地限制它的適用范圍和實(shí)際性能。因此,本文以多塊嵌入、圖增強(qiáng)方法、標(biāo)簽核技術(shù)、模糊投影、核排列以及超分辨率重構(gòu)等為研究對(duì)象,深入探索了多視圖典型相關(guān)分析理論,并提出了一系列多視圖特征抽取方法,為一些實(shí)際應(yīng)用提供了有效的解決方案。本文的主要?jiǎng)?chuàng)新性工作和研究成果如下:(1)提出了多塊嵌入的多視圖典型相關(guān)分析方法。在已有的局部相關(guān)分析方法中,存在單類局部近鄰關(guān)系難以揭示原始高維數(shù)據(jù)內(nèi)在流行的問題,以及監(jiān)督引入導(dǎo)致整體局部信息缺失的問題。為此,本文以局部塊為基礎(chǔ),深入研究了多視圖相關(guān)分析框架下的多源塊結(jié)構(gòu)和多源融合理論,并提出了多塊嵌入的多視圖典型相關(guān)分析方法。該方法不僅能夠自動(dòng)地學(xué)習(xí)更有益于相關(guān)特征抽取的整體局部信息,而且能夠借助視圖內(nèi)散布結(jié)構(gòu)進(jìn)一步增強(qiáng)相關(guān)特征的類分離性。在近紅外圖像、人臉圖像和多視角汽車圖像上的實(shí)驗(yàn)結(jié)果顯示,該方法擁有良好的圖像識(shí)別性能和參數(shù)魯棒性。(2)提出了圖增強(qiáng)多視圖鑒別相關(guān)分析方法。為了解決多視圖相關(guān)分析中圖難以很好掌握數(shù)據(jù)本質(zhì)幾何結(jié)構(gòu)的問題,本文提出了基于數(shù)據(jù)空間分割和監(jiān)督概率整合模型的圖增強(qiáng)方法,該方法構(gòu)建的增強(qiáng)圖能夠更好地反映本質(zhì)幾何結(jié)構(gòu)和鑒別分布信息。在增強(qiáng)圖的基礎(chǔ)上,進(jìn)一步探索多視圖相關(guān)分析框架下圖的有效嵌入和監(jiān)督信息的指導(dǎo),并提出了圖增強(qiáng)多視圖鑒別相關(guān)分析方法。該方法構(gòu)建了視圖間類內(nèi)和類間的增強(qiáng)相關(guān)性,并且考慮了能夠更好掌握視圖內(nèi)和視圖間散布結(jié)構(gòu)的全局散布。大量實(shí)驗(yàn)結(jié)果不僅揭示了圖增強(qiáng)方法的有效性,而且顯示了圖增強(qiáng)多視圖鑒別相關(guān)分析方法在識(shí)別任務(wù)中的優(yōu)越性。(3)提出了新的標(biāo)簽核以及多視圖標(biāo)簽核相關(guān)分析算法。針對(duì)經(jīng)驗(yàn)核方法的監(jiān)督缺失問題,本文提出了一種標(biāo)簽核方法,該方法借助標(biāo)簽指導(dǎo)的單位超球模型很好地保留了類標(biāo)簽信息的鑒別力,然而該方法的標(biāo)簽依賴性導(dǎo)致其難以實(shí)現(xiàn)外樣本擴(kuò)展,為此,利用樣本分布的相似準(zhǔn)則進(jìn)一步構(gòu)建了標(biāo)簽核方法的投影輔助策略,即模糊投影策略。通過將標(biāo)簽核方法和模糊投影策略自然地融入多視圖相關(guān)分析理論,進(jìn)一步提出了多視圖標(biāo)簽核相關(guān)分析算法,該算法借助標(biāo)簽核方法和模糊投影策略的優(yōu)勢(shì)能夠從多視圖數(shù)據(jù)中學(xué)習(xí)帶有強(qiáng)鑒別力的非線性相關(guān)特征。另外,在五個(gè)不同的數(shù)據(jù)集上,分析了近鄰參數(shù)對(duì)模糊投影性能的影響和多視圖標(biāo)簽核相關(guān)分析算法的識(shí)別性能。(4)提出了核排列的多視圖典型相關(guān)分析方法。針對(duì)單核的性能局限性和難以為具體數(shù)據(jù)選擇適應(yīng)核的困境,本文使用核排列將原始的特征向量轉(zhuǎn)化為二維特征矩陣,并依此提出了核排列的多視圖典型相關(guān)分析方法。該方法能夠同時(shí)利用大量的核來揭示每個(gè)視圖更真實(shí)的數(shù)據(jù)分布信息,并且能夠?yàn)槊總�(gè)視圖自動(dòng)地學(xué)習(xí)一個(gè)具有數(shù)據(jù)適應(yīng)性的混合核。此外,該方法的核擴(kuò)展技術(shù)能夠在其他特征抽取方法中直接使用,具有很好的遷移性。在近紅外人臉圖像、熱紅外人臉圖像、可見光人臉圖像、手寫體圖像和目標(biāo)圖像上的實(shí)驗(yàn)表明,提出的方法具有良好的圖像識(shí)別性能。(5)提出了基于局部多視圖一致子空間學(xué)習(xí)的超分辨率方法。在超分辨率重構(gòu)中,為了更好地滿足相似局部假設(shè),本文提出了一種局部多視圖一致子空間學(xué)習(xí)方法;為了能夠同時(shí)使用多類低分辨率圖像進(jìn)行超分辨率重構(gòu),提出了多相關(guān)融合策略;為了確定更準(zhǔn)確的近鄰域,提出了重構(gòu)校對(duì)策略。然后以此為基礎(chǔ),形成一種新的人臉超分辨率重構(gòu)方法,即基于局部多視圖一致子空間學(xué)習(xí)的超分辨率方法。該方法解決了現(xiàn)存基于相關(guān)分析理論的超分辨率重構(gòu)方法無法處理的一些關(guān)鍵問題,在相關(guān)分析理論框架下首次實(shí)現(xiàn)了圖像超分辨率的多源重構(gòu)。此外,針對(duì)殘差補(bǔ)償?shù)谋匾�、多相關(guān)融合策略的有效性、局部多視圖一致子空間的優(yōu)越性、參數(shù)的影響、訓(xùn)練圖像數(shù)量的影響以及最終重構(gòu)圖像的質(zhì)量等設(shè)計(jì)了大量實(shí)驗(yàn),所有的實(shí)驗(yàn)結(jié)果可以給出一個(gè)合理的觀察,即基于局部多視圖一致子空間學(xué)習(xí)的超分辨率方法是一種有效的人臉超分辨率重構(gòu)方法。
[Abstract]:Multi view data is widely used in practical applications, and feature extraction for multi view data has gradually become a hot and key problem in the field of pattern recognition. As a very important multi view feature extraction method, multi view canonical correlation analysis has received extensive attention at home and abroad. Often complex nonlinear data, multi view canonical correlation analysis is difficult to effectively deal with the actual data, which will greatly limit its scope of application and practical performance. Therefore, this paper is based on multi block embedding, graph enhancement method, label kernel technology, fuzzy projection, kernel row and super-resolution reconstruction, and so on. A series of multi view feature extraction methods are proposed to provide effective solutions for some practical applications. The main innovative work and research results of this paper are as follows: (1) a multi block multi view canonical correlation analysis method is proposed. In the existing local correlation analysis methods, there are some existing methods. The single class local neighborhood relationship is difficult to reveal the inherent problem of the original high dimensional data and the problem of the lack of local information. This paper, based on the local block, deeply studies the multi source block structure and multi source fusion theory under the framework of multi view correlation analysis, and puts forward a multi block multi view typical phase. The method not only can automatically learn the whole local information that is more beneficial to the correlation feature extraction, but also can further enhance the class separability of the related features with the aid of the scatter structure in the view. The results show that the method has good image recognition in the near infrared image, face image and multi view vehicle image. Performance and parameter robustness. (2) a graph enhancement multi view identification correlation analysis method is proposed. In order to solve the problem that the graph is difficult to master the essential geometric structure of the data in the multi view correlation analysis, this paper proposes a graph enhancement method based on the data space segmentation and the supervision probability integration model, and the enhanced graph constructed by this method can be better. To reflect the essential geometric structure and the differential distribution information. On the basis of the enhancement graph, we further explore the guidance of the effective embedding and monitoring information under the multi view correlation analysis framework, and propose a graph enhanced multi view identification correlation analysis method. This method constructs the enhanced correlation between classes and classes between views, and considers it possible A large number of experimental results not only reveal the effectiveness of the image enhancement method, but also show the superiority of the image enhancement multi view identification correlation analysis method in the recognition task. (3) a new label kernel and a multi view sign kernel correlation analysis algorithm are proposed. A label kernel method is proposed in this paper. This method preserves the discriminative ability of the class label information with the help of the unit super ball model guided by the label. However, the label dependence of the method makes it difficult to realize the expansion of the outer sample. Therefore, the label kernel method is further constructed by using the similarity criterion of the sample distribution. The projection assistant strategy, that is, the fuzzy projection strategy. By integrating the label kernel method and the fuzzy projection strategy naturally into the multi view correlation analysis theory, the algorithm of multi view sign kernel correlation analysis is further proposed. The algorithm can learn from the multi view data with the advantage of the advantage of the label kernel method and the fuzzy projection strategy. In addition, on five different data sets, the influence of near neighbor parameters on the fuzzy projection performance and the recognition performance of the multi view sign kernel correlation analysis algorithm are analyzed. (4) a multi view canonical correlation analysis method for kernel arrangement is proposed. In this paper, a kernel arrangement is used to transform the original eigenvector into a two-dimensional feature matrix, and a multi view canonical correlation analysis method is proposed. This method can simultaneously use a large number of kernel to reveal the more real data distribution information of each view, and can automatically learn a data adaptation for each view. In addition, the kernel expansion technique of this method can be used directly in other feature extraction methods and has good mobility. Experiments on near infrared face image, hot infrared face image, visible light face image, handwritten image and target image show that the proposed method has good image recognition performance. (5) A super-resolution method based on local multi view uniform subspace learning is proposed. In order to better satisfy similar local assumptions in super-resolution reconstruction, a local multi view uniform subspace learning method is proposed in this paper. In order to be able to use multiple class of low resolution images at the same time for super-resolution reconstruction, a multi correlation fusion is proposed. In order to determine a more accurate near neighbourhood, the reconstructing proofreading strategy is proposed. On the basis of this, a new method of face superresolution reconstruction is formed, which is a super-resolution method based on local multi view uniform subspace learning. This method solves the problem that the existing superresolution reconstruction method based on the correlation analysis theory can not be processed. For the first time, the multi source reconstruction of image superresolution is realized in the framework of correlation analysis theory. In addition, in view of the necessity of the residual compensation, the validity of the multi correlation fusion strategy, the superiority of the local multi view uniform subspace, the influence of the parameters, the influence of the number of the training images and the quality of the final reconstructed image In a large number of experiments, all the experimental results can give a reasonable observation, that is, the super-resolution method based on local multi view uniform subspace learning is an effective method of face super-resolution reconstruction.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
本文編號(hào)：2152748
[Abstract]:Multi view data is widely used in practical applications, and feature extraction for multi view data has gradually become a hot and key problem in the field of pattern recognition. As a very important multi view feature extraction method, multi view canonical correlation analysis has received extensive attention at home and abroad. Often complex nonlinear data, multi view canonical correlation analysis is difficult to effectively deal with the actual data, which will greatly limit its scope of application and practical performance. Therefore, this paper is based on multi block embedding, graph enhancement method, label kernel technology, fuzzy projection, kernel row and super-resolution reconstruction, and so on. A series of multi view feature extraction methods are proposed to provide effective solutions for some practical applications. The main innovative work and research results of this paper are as follows: (1) a multi block multi view canonical correlation analysis method is proposed. In the existing local correlation analysis methods, there are some existing methods. The single class local neighborhood relationship is difficult to reveal the inherent problem of the original high dimensional data and the problem of the lack of local information. This paper, based on the local block, deeply studies the multi source block structure and multi source fusion theory under the framework of multi view correlation analysis, and puts forward a multi block multi view typical phase. The method not only can automatically learn the whole local information that is more beneficial to the correlation feature extraction, but also can further enhance the class separability of the related features with the aid of the scatter structure in the view. The results show that the method has good image recognition in the near infrared image, face image and multi view vehicle image. Performance and parameter robustness. (2) a graph enhancement multi view identification correlation analysis method is proposed. In order to solve the problem that the graph is difficult to master the essential geometric structure of the data in the multi view correlation analysis, this paper proposes a graph enhancement method based on the data space segmentation and the supervision probability integration model, and the enhanced graph constructed by this method can be better. To reflect the essential geometric structure and the differential distribution information. On the basis of the enhancement graph, we further explore the guidance of the effective embedding and monitoring information under the multi view correlation analysis framework, and propose a graph enhanced multi view identification correlation analysis method. This method constructs the enhanced correlation between classes and classes between views, and considers it possible A large number of experimental results not only reveal the effectiveness of the image enhancement method, but also show the superiority of the image enhancement multi view identification correlation analysis method in the recognition task. (3) a new label kernel and a multi view sign kernel correlation analysis algorithm are proposed. A label kernel method is proposed in this paper. This method preserves the discriminative ability of the class label information with the help of the unit super ball model guided by the label. However, the label dependence of the method makes it difficult to realize the expansion of the outer sample. Therefore, the label kernel method is further constructed by using the similarity criterion of the sample distribution. The projection assistant strategy, that is, the fuzzy projection strategy. By integrating the label kernel method and the fuzzy projection strategy naturally into the multi view correlation analysis theory, the algorithm of multi view sign kernel correlation analysis is further proposed. The algorithm can learn from the multi view data with the advantage of the advantage of the label kernel method and the fuzzy projection strategy. In addition, on five different data sets, the influence of near neighbor parameters on the fuzzy projection performance and the recognition performance of the multi view sign kernel correlation analysis algorithm are analyzed. (4) a multi view canonical correlation analysis method for kernel arrangement is proposed. In this paper, a kernel arrangement is used to transform the original eigenvector into a two-dimensional feature matrix, and a multi view canonical correlation analysis method is proposed. This method can simultaneously use a large number of kernel to reveal the more real data distribution information of each view, and can automatically learn a data adaptation for each view. In addition, the kernel expansion technique of this method can be used directly in other feature extraction methods and has good mobility. Experiments on near infrared face image, hot infrared face image, visible light face image, handwritten image and target image show that the proposed method has good image recognition performance. (5) A super-resolution method based on local multi view uniform subspace learning is proposed. In order to better satisfy similar local assumptions in super-resolution reconstruction, a local multi view uniform subspace learning method is proposed in this paper. In order to be able to use multiple class of low resolution images at the same time for super-resolution reconstruction, a multi correlation fusion is proposed. In order to determine a more accurate near neighbourhood, the reconstructing proofreading strategy is proposed. On the basis of this, a new method of face superresolution reconstruction is formed, which is a super-resolution method based on local multi view uniform subspace learning. This method solves the problem that the existing superresolution reconstruction method based on the correlation analysis theory can not be processed. For the first time, the multi source reconstruction of image superresolution is realized in the framework of correlation analysis theory. In addition, in view of the necessity of the residual compensation, the validity of the multi correlation fusion strategy, the superiority of the local multi view uniform subspace, the influence of the parameters, the influence of the number of the training images and the quality of the final reconstructed image In a large number of experiments, all the experimental results can give a reasonable observation, that is, the super-resolution method based on local multi view uniform subspace learning is an effective method of face super-resolution reconstruction.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
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