本文選題：紋理 + 特征提取　； 參考：《華東師范大學》2017年博士論文

【摘要】：紋理圖像特征提取和分類在遙感、醫(yī)學、農業(yè)、工業(yè)等領域有廣泛應用,可以進行地形地貌檢測、災害預防、農作物監(jiān)測、醫(yī)學影像分析等。傳統(tǒng)的紋理特征還存在一些不足,一些紋理特征對旋轉、姿態(tài)、視角、尺度變化等較為敏感,分類時間較長,在一些實際應用中,這些紋理特征分類效果較差。針對紋理圖像中旋轉問題,本文提出一種新的多尺度旋轉不變紋理特征(MSRIT)提取方法,MSRIT具有旋轉不變性,可應用于旋轉、視角、姿態(tài)、尺度變化等紋理分類。針對傳統(tǒng)分類方法在紋理圖像分類效率較低問題,本文提出了一種新的SVM分類模型(SVMpdip),并提出了針對該模型的求解方法:基于塊消除法的原-對偶內點法(PDIPbe)。SVMpdip具有很高的分類準確率,且分類時間少于一些傳統(tǒng)分類方法。MSRIT和SVMpdip方法可以處理實際應用中較為復雜的紋理分類問題。MSRIT方法是從多個尺度圖像的多個旋轉不變局部特征描述子中來提取圖像紋理特征,這些紋理特征具有多尺度旋轉不變性。在SVMpdip模型求解過程中,本文采用塊消除法將中間過程系數矩陣分解為含有單位矩陣、對角矩陣等多個特殊矩陣的塊矩陣,大大減少存儲空間,減少了計算復雜度,提高分類效率。本文先尋找分類模型合適優(yōu)化初始點,再求解分類模型,提高了模型求解收斂速度。在理論分析基礎上,本文利用國際上典型紋理數據集進行了較多實驗分析與評價,實驗結果表明MSRIT分類準確度好于Gabor、GLCM、GLDM、LBP等傳統(tǒng)紋理特征提取方法;SVMpdip 分類時間短于 SMO-P、SMO-K1、SMO-K2、CVX、quadprog、svmlight 等分類方法,效率高于它們,分類準確率也非常高。
[Abstract]:Texture image feature extraction and classification are widely used in remote sensing, medicine, agriculture, industry and other fields. They can be used for terrain and geomorphology detection, disaster prevention, crop monitoring, medical image analysis and so on. Some of the traditional texture features are sensitive to rotation, attitude, visual angle, scale change and so on, and the classification time is longer. In some practical applications, the classification effect of these texture features is poor. In this paper, a new multi-scale rotation invariant texture feature (MSRIT) extraction method is proposed to solve the rotation problem in texture images. MSRIT is rotation-invariant and can be applied to texture classification such as rotation, angle of view, attitude, scale change and so on. Aiming at the low efficiency of traditional classification methods in texture image classification, In this paper, a new SVM classification model (SVMpdip) is proposed, and a method for solving the model is proposed: the primal-dual interior point method (PDIPbe). SVMpdip based on block elimination method has high classification accuracy. The classification time is less than that of some traditional classification methods. MSRIT and SVMpdip can deal with the more complex texture classification problem in practical applications. MSRIT can extract image texture features from multiple rotation invariant local feature descriptors of multi-scale images. These texture features have multi-scale rotation invariance. In the process of solving SVMpdip model, the intermediate process coefficient matrix is decomposed into block matrices containing unit matrix, diagonal matrix and other special matrices by block elimination method, which greatly reduces the storage space and computational complexity. Improve the efficiency of classification. In this paper, the optimal initial point of the classification model is found first, and then the classification model is solved, which improves the convergence rate of the model. On the basis of theoretical analysis, more experiments are carried out with typical texture data sets in the world. The experimental results show that the accuracy of MSRIT classification is better than that of traditional texture feature extraction methods, such as Gabor-GLCM-GLDMU LBP, and the time of SVMpdip classification is shorter than that of SMO-PMO-K1 SMO-K2CVXMlight and other classification methods, such as SMO-PMO-K1CMO-K2CVXMlight, etc. The efficiency is higher than them, and the classification accuracy is very high.
【學位授予單位】：華東師范大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：2064121