本文關(guān)鍵詞：基于顏色字典和Doublet特征優(yōu)化的舌像分類方法研究　出處：《華東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 模式識別 圖像處理 特征優(yōu)化 舌像

【摘要】：自動化舌診系統(tǒng)克服了傳統(tǒng)的主觀化和非量化等缺點,對舌診具有重要意義和參考價值。但是舌診系統(tǒng)往往受諸多因素影響,主要表現(xiàn)在兩個方面,分別是特征提取方法和分類策略。針對以上兩種問題,本文主要做了兩方面工作:(1)在特征提取方面,本文提出了一種基于顏色字典特征提取方法和基于Doublet的特征優(yōu)化方法;(2)在分類器方面，，采用了高效的GBDT分類器。基于顏色字典的特征提取方法包括顏色字典的定義和特征提取。分析了舌像在CIELab色彩空間的色域,并根據(jù)中醫(yī)專家總結(jié)出的舌像主要顏色,定義了舌像的顏色字典;在特征提取過程中,通過對重疊分割方法得到的圖像塊與顏色字典之間進行相似性分析,提取分割塊的類別直方圖特征,并組合成為舌像特征。在特征提取過程中,由于采用了重疊分割法,保持了圖像信息的完整性;并且提取的是圖像局部特征,使得圖像在縮放時能保持特征不變性。Doublet特征優(yōu)化方法對基于顏色字典的特征進行了優(yōu)化。首先利用舌像之間的相似性構(gòu)建Doublet,定義了新的樣本特征和類別;然后利用多項式核函數(shù)模型為Doublet定義新的核函數(shù),并利用分類器訓(xùn)練得到關(guān)于樣本Doublet的模型,由該模型重新構(gòu)建原有未經(jīng)過Doublet處理的樣本,從而實現(xiàn)對原始樣本特征的優(yōu)化。在分類策略上,選擇梯度提升決策樹(Gradient Boosting Decision Tree,GBDT)分類器。GBDT是一種基于多個弱分類器組合的分類器,利用殘差作為損失函數(shù)。其中,弱分類器采用的是分類回歸樹。為了證明方法的有效性,在舌像數(shù)據(jù)集上進行了驗證。實驗結(jié)果表明,基于顏色字典特征提取方法提取到的特征具有較強的魯棒性,Doublet多核特征優(yōu)化方法減少了特征的噪聲干擾,而GBDT相比于支持向量機等分類器對舌像分類時具有較高的有較高的分類正確率和特異性。
[Abstract]:The automatic tongue diagnosis system overcomes the shortcomings of the traditional subjective and non quantified, which is of great significance and reference value for the diagnosis of the tongue. However, the tongue diagnosis system is often affected by many factors, mainly in two aspects, the feature extraction method and the classification strategy. In view of the above two problems, this paper mainly does two aspects: (1) in aspect of feature extraction, this paper proposes a feature extraction method based on color dictionary and Doublet based feature optimization. (2) in the aspect of classifier, an efficient GBDT classifier is adopted. The method of feature extraction based on color dictionary includes the definition and feature extraction of color dictionary. Analysis of tongue image in CIELab color space and color gamut, according to Chinese experts summed up the main tongue color, tongue color is defined in the dictionary; feature extraction process, similarity analysis between image blocks to get through the overlapping segmentation and color dictionary extraction category histogram segmentation block. And as the tongue image feature. In the process of feature extraction, the overlapped segmentation method is adopted to preserve the integrity of image information, and extract the local features of the image, so that the feature invariance can be maintained when the image is zoomed. The Doublet feature optimization method optimizes the feature based on the color dictionary. Firstly, the similarity between the construction of Doublet tongue, defines the features and categories of new samples; and then define the new kernel function for Doublet with polynomial kernel function model, and the use of a classifier training samples of Doublet model, the model constructs the original sample without Doublet treatment, so as to realize the optimization of the original sample the characteristics of the. In the classification strategy, the Gradient Boosting Decision Tree (GBDT) classifier is selected. GBDT is a classifier based on multiple weak classifier combination, which uses the residual as a loss function. Among them, the weak classifier uses the classified regression tree. In order to prove the validity of the method, it is verified on the data set of the tongue. The experimental results show that the characteristics of the color feature extraction method to extract the dictionary based on robust, Doublet kernel feature optimization method to reduce the noise interference characteristics, and GBDT compared to the support vector machine classifier for classification of tongue image has higher classification accuracy and high specificity.
【學(xué)位授予單位】：華東師范大學(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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