本文選題：兜蘭花　切入點(diǎn)：顏色矩　出處：《云南大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：花卉分類是圖像處理以及自動化分類領(lǐng)域研究的主要研究課題之一。多年來研究人員在這個課題中投入了大量精力,同時也取得不少的研究成果。顯然,在未知花卉品種的情況下,只依據(jù)花卉圖片來識別花卉本身是很有挑戰(zhàn)的問題。通常,自然環(huán)境中的花卉,在不同的天氣和時間,所接收的光照不同,而且同一種花也會有大小和質(zhì)地之間的變化,這些因素往往會影響花卉分類的結(jié)果。如何解決這些問題并最終實(shí)現(xiàn)花卉的自動分類,對于那些對花卉感興趣的用戶具有極大的意義,同時也能幫助植物學(xué)家和花卉專家高效準(zhǔn)確地識別花卉種類。對于花卉的分類問題,論文中選擇了一種在泰國很有名的兜蘭作為研究對象。兜蘭是一種瀕危植物,它是蘭花的一種,花色豐富,花型奇特,非常有趣的是它一株只開一朵花。但是不同種類的兜蘭卻有著相似的外觀,這使得對兜蘭進(jìn)行分類難度很大。為了實(shí)現(xiàn)兜蘭的分類,論文利用多層神經(jīng)網(wǎng)絡(luò)分類器模型,根據(jù)花卉圖片的視覺內(nèi)容進(jìn)行分類,選取兜蘭的顏色特征以及基于分割的分形紋理分析(SFTA)特征作為分類的特征向量。論文的主要貢獻(xiàn)如下：(1)建立兜蘭花數(shù)據(jù)庫。數(shù)據(jù)庫包含1100幅11種兜蘭花圖片,與前人的數(shù)據(jù)集(200幅5種兜蘭花)相比,包含的品種更多。此外,兜蘭花在中國較為稀少,由于缺少樣本,相關(guān)的花卉分類研究通常會選擇常見的花卉品種,這使得兜蘭花的分類研究成了新課題。(2)提出了一種新的兜蘭花分類模型一基于多層神經(jīng)網(wǎng)絡(luò)的兜蘭花分類模型。在分類模型的特征選擇上,論文提取了多種特征。在顏色特征上,用到了2種顏色特征：顏色矩和顏色直方圖,實(shí)驗(yàn)中對比了多種顏色空間如RGB, CIE XYZ, YCbCr以及HSV顏色空間在分類器上的表現(xiàn),實(shí)驗(yàn)結(jié)果表明：使用在HSV顏色空間下的顏色特征分類效果最理想；在紋理特征提取上采用基于分割的分形紋理分析(SFTA)算法,該算法與以前的紋理特征提取算法相比,在質(zhì)量和效率上表現(xiàn)更優(yōu)。在分類器的選擇上,先前的許多研究工作用到的分類器,有隨機(jī)森林,支持向量機(jī)(sVM),人工神經(jīng)網(wǎng)絡(luò)(ANN)等。除此之外,論文還對比了一些著名的分類器例如樸素貝葉斯算法、 k近鄰分類算法、C4.5決策樹算法(J48),序列最小優(yōu)化SMO算法和多層神經(jīng)網(wǎng)絡(luò)(MLP)。實(shí)驗(yàn)結(jié)果顯示,MLP分類器在所有的分類器中表現(xiàn)最為理想,它的平均分類準(zhǔn)確率達(dá)到了97.64%,所以本論文最終選用MLP分類器。論文的實(shí)驗(yàn)結(jié)果表明,兜蘭花分類模型的準(zhǔn)確率令人滿意,該模型能幫助植物學(xué)家對兜蘭進(jìn)行識別分類,并能為植物學(xué)家選種育種提供幫助。在未來,可將該模型應(yīng)用于花卉圖片檢索以及對不同類型的花卉分類工作,而且很容易拓展到類似的應(yīng)用中。
[Abstract]:Flower classification is one of the main research topics in the field of image processing and automatic classification. Over the years, researchers have invested a lot of energy in this subject, and have also achieved a lot of research results. In the case of unknown varieties of flowers, it is a challenging problem to identify flowers only on the basis of pictures of flowers. Usually, flowers in the natural environment receive different light in different weather and time. And the same kind of flower also has the change between the size and the texture, these factors often influence the flower classification result. How to solve these problems and finally realize the automatic classification of flowers, It is of great significance to those who are interested in flowers and can also help botanists and florists identify flower species efficiently and accurately. In this paper, we selected a kind of Daurus, which is very famous in Thailand, as an endangered plant. It is a kind of orchid with rich flowers and peculiar flowers. It is very interesting that it has only one flower per plant. However, the different species have similar appearance, which makes it very difficult to classify the orchid. In order to realize the classification, the paper uses the multi-layer neural network classifier model. According to the visual content of flower pictures, The color features of Cymbidium and the fractal texture analysis (SFTA) feature based on segmentation are selected as the feature vectors of the classification. The main contributions of this paper are as follows: 1: 1) the database is established. The database contains 1100 pictures of 11 species of Cymbidium. It contains more varieties than previous data sets of 200 pieces of five species of cymbidium. In addition, the orchids are rare in China, and because of the lack of samples, related flower classification studies usually select common flower varieties. This makes the research of orchid classification become a new subject. (2) A new classification model of orchid, a classification model based on multi-layer neural network, is proposed. In this paper, two kinds of color features are used: color moment and color histogram. In the experiment, the performance of many color spaces such as RGB, CIE XYZ, YCbCr and HSV color space in classifier is compared. The experimental results show that the color feature classification in HSV color space is the best, and the fractal texture analysis algorithm based on segmentation is used in texture feature extraction, which is compared with the previous texture feature extraction algorithm. Better performance in quality and efficiency. In the selection of classifiers, many previous studies have used classifiers such as random forests, support vector machines (SVMs), artificial neural networks (Ann), etc. The paper also compares some famous classifiers such as naive Bayes algorithm, k-nearest neighbor classification algorithm, C4.5 decision tree algorithm, sequence minimum optimization SMO algorithm and multilayer neural network. The best performance in the class, Its average classification accuracy is 97.64%, so the MLP classifier is used in this paper. The experimental results show that the accuracy of the orchid classification model is satisfactory, and the model can help botanists to recognize and classify the orchid. In the future, the model can be applied to the image retrieval of flowers and the classification of different types of flowers, and can be easily extended to similar applications.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 謝曉東;面向花卉圖像的精細(xì)圖像分類研究[D];廈門大學(xué);2014年

2 裴勇;基于數(shù)字圖像的花卉種類識別技術(shù)研究[D];北京林業(yè)大學(xué);2011年

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本文編號：1571509