張量主成分分析及在圖像序列識別中的應(yīng)用
發(fā)布時間：2018-08-23 12:55
【摘要】：圖像序列,如視頻圖像、醫(yī)學(xué)圖像、高光譜遙感影像等都屬于三維張量。張量本質(zhì)上是多維數(shù)組,它是矩陣的多線性推廣。圖像序列不僅成為人類活動中最常用的信息載體,而且在張量模式下對圖像序列的識別也成為近幾年來模式識別領(lǐng)域研究的熱點問題。在特征提取中,最常用的方法是主成分分析法(Principal Component Analysis,PCA)。由于圖像序列屬于三維張量,本文考慮使用張量主成分分析(Tensor Principal Component Analysis,TPCA)的方法對圖像序列進(jìn)行識別。然而現(xiàn)有的張量主成分分析的方法在特征提取時無法在張量模式下確定一個合適的奇異值閾值,從而無法確定圖像序列的特征保存率。在分類識別中,現(xiàn)有的張量型分類器僅能處理二維數(shù)據(jù),無法對多維特征進(jìn)行直接分類。這種所謂的張量型分類器目前只能局限于二維,本質(zhì)上是二維張量型分類器。針對以上問題,本文在張量模式下對圖像序列的識別進(jìn)行研究,具體內(nèi)容如下:首先,現(xiàn)有的TPCA方法存在缺陷:無法在張量模式下確定一個合適的奇異值閾值,即在張量模式下無法找出去除噪聲和保留細(xì)節(jié)之間的平衡點。本文在TPCA的基礎(chǔ)上提出了截斷張量主成分分析(Truncated Tensor Principal Component Analysis,TTPCA)的方法來確定一個合適的奇異值閾值。奇異值閾值的確定用來濾除較小的奇異值、保留較大的奇異值,從而找到去除噪聲和保留細(xì)節(jié)的平衡點,完成對圖像序列的特征提取。然后,為了有效地提高圖像序列的識別精度,考慮到經(jīng)過特征提取后的圖像序列依然是張量模式,提出張量模式下的三維支持張量機(jī)(Three Dimensional Support Tensor Machine,3DSTM)對張量型數(shù)據(jù)進(jìn)行直接分類,避免了將張量數(shù)據(jù)矢量化。關(guān)于張量模式的分類器主要做了如下工作:第一,在3DSTM算法中,利用張量乘法的運算規(guī)則,改進(jìn)傳統(tǒng)的支持向量機(jī)和二維的支持張量機(jī),把它們擴(kuò)展到理論上的N維,使其可以直接處理張量模式的輸入;第二,在3DSTM分類器模型的基礎(chǔ)上,根據(jù)SVM和3DSTM兩者的優(yōu)缺點,引入多秩的思想,提出基于多秩三維支持張量機(jī)(Multiple Rank Three Dimensional Support Tensor Machine,MR3DSTM)分類器的設(shè)計,使圖像序列的識別率更高。用本文提出的TTPCA和3DSTM,以及TTPCA和MR3DSTM這兩種張量形式的分類識別算法與目前流行的兩種算法進(jìn)行實驗對比。實驗結(jié)果表明,本文提出的算法在圖像序列的識別精度和速度上有明顯提高;并且本文提出的MR3DSTM和3DSTM這兩種分類器方法相比,MR3DSTM的識別精度更高。
[Abstract]:Image sequences, such as video images, medical images and hyperspectral remote sensing images, all belong to 3D Zhang Liang. Zhang Liang is essentially a multidimensional array, which is a multilinear generalization of a matrix. Image sequences have not only become the most commonly used information carrier in human activities, but also the recognition of image sequences in Zhang Liang mode has become a hot topic in the field of pattern recognition in recent years. In feature extraction, principal component analysis (Principal Component) is the most commonly used method. Because the image sequence belongs to three dimensional Zhang Liang, the method of Zhang Liang principal component analysis (Zhang Liang) is considered to recognize the image sequence. However, the existing Zhang Liang principal component analysis method can not determine a suitable singular value threshold in Zhang Liang mode when feature extraction, thus can not determine the feature preservation rate of image sequence. In classification and recognition, the existing Zhang Liang classifier can only deal with two-dimensional data, and can not directly classify multidimensional features. The so-called Zhang Liang classifier can only be confined to two-dimensional, essentially two-dimensional Zhang Liang-type classifier. Aiming at the above problems, this paper studies the recognition of image sequences in Zhang Liang mode. The main contents are as follows: firstly, the existing TPCA methods have some defects: it is impossible to determine a suitable singular value threshold in Zhang Liang mode. That is, the balance between noise removal and detail retention can not be found in Zhang Liang mode. In this paper, we propose a truncated Zhang Liang principal component analysis (Zhang Liang) method based on TPCA to determine an appropriate singular value threshold. The determination of the singular value threshold is used to filter the smaller singular value and retain the larger singular value, so as to find the equilibrium point to remove noise and preserve the details, and to complete the feature extraction of the image sequence. Then, in order to improve the recognition accuracy of image sequence effectively, considering that the image sequence after feature extraction is still Zhang Liang pattern, a 3D support Zhang Liang machine (Three Dimensional Support Tensor Machine 3DSTM) based on Zhang Liang mode is proposed to classify Zhang Liang data directly. Avoid vectorization of Zhang Liang data. The main work of the classifier about Zhang Liang pattern is as follows: first, in the 3DSTM algorithm, the traditional support vector machine and the two-dimensional support Zhang Liang machine are improved by using the operation rules of Zhang Liang multiplication, and they are extended to the N-dimension in theory. It can directly handle the input of Zhang Liang mode. Secondly, on the basis of 3DSTM classifier model, according to the advantages and disadvantages of SVM and 3DSTM, the idea of multi-rank is introduced. A new classifier based on multi-rank Zhang Liang machine (Multiple Rank Three Dimensional Support Tensor Machine MR3DSTM is proposed, which makes the recognition rate of image sequence higher. The classification and recognition algorithms of TTPCA and 3DSTM, as well as TTPCA and MR3DSTM, which are proposed in this paper, are compared with the two popular algorithms. The experimental results show that the proposed algorithm improves the recognition accuracy and speed of image sequences obviously, and the two classifiers, MR3DSTM and 3DSTM, have higher recognition accuracy than MR3DSTM.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2199142
[Abstract]:Image sequences, such as video images, medical images and hyperspectral remote sensing images, all belong to 3D Zhang Liang. Zhang Liang is essentially a multidimensional array, which is a multilinear generalization of a matrix. Image sequences have not only become the most commonly used information carrier in human activities, but also the recognition of image sequences in Zhang Liang mode has become a hot topic in the field of pattern recognition in recent years. In feature extraction, principal component analysis (Principal Component) is the most commonly used method. Because the image sequence belongs to three dimensional Zhang Liang, the method of Zhang Liang principal component analysis (Zhang Liang) is considered to recognize the image sequence. However, the existing Zhang Liang principal component analysis method can not determine a suitable singular value threshold in Zhang Liang mode when feature extraction, thus can not determine the feature preservation rate of image sequence. In classification and recognition, the existing Zhang Liang classifier can only deal with two-dimensional data, and can not directly classify multidimensional features. The so-called Zhang Liang classifier can only be confined to two-dimensional, essentially two-dimensional Zhang Liang-type classifier. Aiming at the above problems, this paper studies the recognition of image sequences in Zhang Liang mode. The main contents are as follows: firstly, the existing TPCA methods have some defects: it is impossible to determine a suitable singular value threshold in Zhang Liang mode. That is, the balance between noise removal and detail retention can not be found in Zhang Liang mode. In this paper, we propose a truncated Zhang Liang principal component analysis (Zhang Liang) method based on TPCA to determine an appropriate singular value threshold. The determination of the singular value threshold is used to filter the smaller singular value and retain the larger singular value, so as to find the equilibrium point to remove noise and preserve the details, and to complete the feature extraction of the image sequence. Then, in order to improve the recognition accuracy of image sequence effectively, considering that the image sequence after feature extraction is still Zhang Liang pattern, a 3D support Zhang Liang machine (Three Dimensional Support Tensor Machine 3DSTM) based on Zhang Liang mode is proposed to classify Zhang Liang data directly. Avoid vectorization of Zhang Liang data. The main work of the classifier about Zhang Liang pattern is as follows: first, in the 3DSTM algorithm, the traditional support vector machine and the two-dimensional support Zhang Liang machine are improved by using the operation rules of Zhang Liang multiplication, and they are extended to the N-dimension in theory. It can directly handle the input of Zhang Liang mode. Secondly, on the basis of 3DSTM classifier model, according to the advantages and disadvantages of SVM and 3DSTM, the idea of multi-rank is introduced. A new classifier based on multi-rank Zhang Liang machine (Multiple Rank Three Dimensional Support Tensor Machine MR3DSTM is proposed, which makes the recognition rate of image sequence higher. The classification and recognition algorithms of TTPCA and 3DSTM, as well as TTPCA and MR3DSTM, which are proposed in this paper, are compared with the two popular algorithms. The experimental results show that the proposed algorithm improves the recognition accuracy and speed of image sequences obviously, and the two classifiers, MR3DSTM and 3DSTM, have higher recognition accuracy than MR3DSTM.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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，本文編號：2199142
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