基于深度學(xué)習(xí)的植物圖像集識別技術(shù)研究
發(fā)布時(shí)間：2018-11-08 18:43
【摘要】：植物分類學(xué)是一門對植物物種進(jìn)行準(zhǔn)確描述,命名,分群歸類,探求各類群之間的親緣關(guān)系,以及演化過程的基礎(chǔ)科學(xué)。隨著模式識別技術(shù)在植物圖像分類任務(wù)中的廣泛應(yīng)用,對植物分類學(xué)的發(fā)展具有促進(jìn)作用,而且給農(nóng)業(yè)的科學(xué)研究帶來了非常大的幫助。相比于傳統(tǒng)的基于單個(gè)或少量圖像的植物圖像分類識別算法,基于圖像集的植物圖像分類算法的關(guān)鍵是如何為圖像集進(jìn)行建模,以及如何度量對圖像集建模后模型之間的相似度。為了更好地對植物圖像進(jìn)行分類識別,有必要對以植物葉片圖像集為研究對象的分類技術(shù)進(jìn)行研究。本文以植物葉片圖像為研究目標(biāo),對非線性重構(gòu)模型、SPCANet模型、KmeansNet模型和利用深度模型對植物圖像進(jìn)行粒度分類等內(nèi)容進(jìn)行詳細(xì)的介紹。本文的主要工作內(nèi)容為:(1)提出一種基于非線性重構(gòu)模型的植物葉片圖像集的分類識別方法。該方法使用高斯受限玻爾茲曼機(jī)(GRBMs)通過非監(jiān)督預(yù)訓(xùn)練來初始化模型的權(quán)值,然后為每一個(gè)植物葉片圖像集用初始化的模型訓(xùn)練得到一個(gè)特定的模型。最后根據(jù)測試樣本的最小重構(gòu)誤差和測試樣本集的最多投票策略來判定測試樣本集的類別。并采用基于k-means的特征提取方法來提取植物葉片圖像特征。(2)提出了一種淺層PCANet(SPCANet)模型的植物圖像集的分類識別方法。該方法首先用SPCANet模型來提取植物圖像的特征,然后用線性SVM分類,最后根據(jù)投票策略判定測試集的類別。該模型是基于卷積神經(jīng)網(wǎng)的結(jié)構(gòu)設(shè)計(jì)的。該模型由卷積濾波層、非線性層和特征提取層三部分組成,其中卷積層的卷積核不同于傳統(tǒng)的深度學(xué)習(xí)網(wǎng)絡(luò),而是通過PCA算法得到,這大大的減少了網(wǎng)絡(luò)的訓(xùn)練時(shí)間和參數(shù)的設(shè)置。(3)提出一種KmeansNet模型的植物圖像集的分類識別方法。該方法是SPCANet模型的變體,不同之處在于卷積層的卷積核是通過Kmeans算法得到。(4)利用深度學(xué)習(xí)Caffe框架對大規(guī)模植物圖像進(jìn)行粒度分類。引入粒度分類的思想為大規(guī)模植物圖像的分類提供了一個(gè)新的思路。在大數(shù)據(jù)的背景下,利用Caffenet模型強(qiáng)大規(guī)模分類能力,通過微調(diào)Caffenet網(wǎng)絡(luò)以實(shí)現(xiàn)對植物圖像分別按門、綱、目、科、屬進(jìn)行粒度分類。
[Abstract]:Plant taxonomy is a basic science for the accurate description, naming, grouping and classification of plant species, exploring the relationship between various groups, and the evolution process. With the wide application of pattern recognition technology in the task of plant image classification, it can promote the development of plant taxonomy and bring great help to the scientific research of agriculture. Compared with the traditional plant image classification and recognition algorithm based on a single or small number of images, the key of the plant image classification algorithm based on image set is how to model the image set. And how to measure the similarity between the models after modeling the image sets. In order to better classify and recognize plant images, it is necessary to study the classification technology of plant leaf image set. In this paper, the nonlinear reconstruction model, SPCANet model, KmeansNet model and granularity classification of plant image by depth model are introduced in detail. The main work of this paper is as follows: (1) A classification and recognition method of plant leaf image set based on nonlinear reconstruction model is proposed. The method uses Gao Si constrained Boltzmann machine (GRBMs) to initialize the weight of the model by unsupervised pre-training, and then trains a specific model for each plant leaf image set with the initialized model. Finally, according to the minimum reconstruction error of the test sample and the maximum voting strategy of the test sample set, the classification of the test sample set is determined. The feature extraction method based on k-means is used to extract the feature of plant leaf image. (2) A classification and recognition method of plant image set based on shallow PCANet (SPCANet) model is proposed. Firstly, the SPCANet model is used to extract the features of plant images, then the linear SVM classification is used. Finally, the classification of the test set is determined according to the voting strategy. The model is based on the structure of the convolutional neural network. The model consists of three parts: convolution filter layer, nonlinear layer and feature extraction layer. The convolution kernel of the convolution layer is different from the traditional depth learning network, but is obtained by PCA algorithm. This greatly reduces the training time and parameter setting of the network. (3) A classification and recognition method of plant image set based on KmeansNet model is proposed. This method is a variant of SPCANet model, the difference is that the convolution kernel of convolution layer is obtained by Kmeans algorithm. (4) granularity classification of large scale plant images is carried out by using depth learning Caffe framework. The idea of granularity classification is introduced to provide a new idea for the classification of large-scale plant images. Under the background of big data, the Caffenet model is used to classify plant images according to door, class, order, family and genus by fine-tuning Caffenet network.
【學(xué)位授予單位】：華僑大學(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：2319335
[Abstract]:Plant taxonomy is a basic science for the accurate description, naming, grouping and classification of plant species, exploring the relationship between various groups, and the evolution process. With the wide application of pattern recognition technology in the task of plant image classification, it can promote the development of plant taxonomy and bring great help to the scientific research of agriculture. Compared with the traditional plant image classification and recognition algorithm based on a single or small number of images, the key of the plant image classification algorithm based on image set is how to model the image set. And how to measure the similarity between the models after modeling the image sets. In order to better classify and recognize plant images, it is necessary to study the classification technology of plant leaf image set. In this paper, the nonlinear reconstruction model, SPCANet model, KmeansNet model and granularity classification of plant image by depth model are introduced in detail. The main work of this paper is as follows: (1) A classification and recognition method of plant leaf image set based on nonlinear reconstruction model is proposed. The method uses Gao Si constrained Boltzmann machine (GRBMs) to initialize the weight of the model by unsupervised pre-training, and then trains a specific model for each plant leaf image set with the initialized model. Finally, according to the minimum reconstruction error of the test sample and the maximum voting strategy of the test sample set, the classification of the test sample set is determined. The feature extraction method based on k-means is used to extract the feature of plant leaf image. (2) A classification and recognition method of plant image set based on shallow PCANet (SPCANet) model is proposed. Firstly, the SPCANet model is used to extract the features of plant images, then the linear SVM classification is used. Finally, the classification of the test set is determined according to the voting strategy. The model is based on the structure of the convolutional neural network. The model consists of three parts: convolution filter layer, nonlinear layer and feature extraction layer. The convolution kernel of the convolution layer is different from the traditional depth learning network, but is obtained by PCA algorithm. This greatly reduces the training time and parameter setting of the network. (3) A classification and recognition method of plant image set based on KmeansNet model is proposed. This method is a variant of SPCANet model, the difference is that the convolution kernel of convolution layer is obtained by Kmeans algorithm. (4) granularity classification of large scale plant images is carried out by using depth learning Caffe framework. The idea of granularity classification is introduced to provide a new idea for the classification of large-scale plant images. Under the background of big data, the Caffenet model is used to classify plant images according to door, class, order, family and genus by fine-tuning Caffenet network.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前3條
1 龔丁禧;曹長榮;;基于卷積神經(jīng)網(wǎng)絡(luò)的植物葉片分類[J];計(jì)算機(jī)與現(xiàn)代化;2014年04期
2 翟傳敏;汪青萍;杜吉祥;;基于葉緣與葉脈分?jǐn)?shù)維特征的植物葉識別方法研究[J];計(jì)算機(jī)科學(xué);2014年02期
3 杜吉祥;汪增福;;基于徑向基概率神經(jīng)網(wǎng)絡(luò)的植物葉片自動識別方法[J];模式識別與人工智能;2008年02期
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