本文關(guān)鍵詞： 圖像分割 元胞自動機(jī) 特征融合 特征加權(quán) 花卉圖像分類　出處：《太原科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著人們生活質(zhì)量的提高,養(yǎng)植花卉成為許多人培養(yǎng)性情的一種愛好。面對如此種類繁多、色彩紛雜的花卉,研究一種有效的花卉圖像分類方法,幫助人們更好地認(rèn)識花卉的一般形態(tài)和生活習(xí)性,提高花卉養(yǎng)植水平,具有重要的研究價值。圖像分割和特征融合是提高花卉圖像分類精度的兩個主要步驟,但是傳統(tǒng)的圖像分割方法常常會因花卉圖像背景過于復(fù)雜而造成分割效果不佳,而且一般的特征融合方法也僅是簡單地把多個特征拼接在一起,并未將不同特征對花卉分類貢獻(xiàn)的不同考慮在內(nèi),從而影響了分類的效果。為進(jìn)一步提高花卉圖像分類的精度,本文提出一種基于元胞自動機(jī)和特征加權(quán)融合的花卉圖像分類方法。主要研究內(nèi)容包括以下三個方面:(1)給出了一種基于元胞自動機(jī)的花卉主體區(qū)域提取方法。該方法首先對花卉圖像進(jìn)行預(yù)處理操作,應(yīng)用SLIC算法將其分割成N個小的超像素點(diǎn),并通過和分類的邊緣種子進(jìn)行顏色空間和距離空間的對比以得到一幅基于背景的顯著圖。其次,根據(jù)相應(yīng)的規(guī)則,在基于背景的顯著圖上使用元胞自動機(jī)的新型傳播機(jī)制得到優(yōu)化的顯著圖。然后,再采用最大類間方差法找到該顯著圖的一個合適的閾值,以完成灰度圖轉(zhuǎn)換為二值圖的操作。最后,在花卉原圖的基礎(chǔ)上將二值圖的白色部分進(jìn)行填充,得到了花卉主體區(qū)域。通過在17-flower數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn),驗(yàn)證了該方法是有效的。(2)給出了一種基于花卉主體區(qū)域的特征加權(quán)融合分類方法。一般的特征融合方法僅是簡單地將多個特征拼接在一起,并未把不同特征對花卉分類貢獻(xiàn)的不同考慮在內(nèi)。為有效提高花卉圖像分類精度,本文首先對上述(1)方法中提取的花卉主體區(qū)域的顏色特征和局部特征進(jìn)行加權(quán)融合,然后利用SVM實(shí)現(xiàn)了花卉圖像分類。最后,通過在13-flower和102-flower圖像數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn),驗(yàn)證了該方法的有效性。(3)開發(fā)了一個基于元胞自動機(jī)和特征加權(quán)融合的花卉圖像分類原型系統(tǒng)。以Matlab7.0和VB6.0作為開發(fā)工具,設(shè)計(jì)并實(shí)現(xiàn)了一個基于元胞自動機(jī)和特征加權(quán)融合的花卉圖像分類原型系統(tǒng)。
[Abstract]:With the improvement of people's quality of life, planting flowers has become a hobby for many people to cultivate temperament. In the face of so many kinds of flowers and colorful flowers, an effective flower image classification method is studied. It has important research value to help people better understand the general form and life habits of flowers and improve the level of flower planting. Image segmentation and feature fusion are the two main steps to improve the classification accuracy of flower images. However, traditional image segmentation methods often result in poor segmentation results due to the complexity of flower image background, and the general feature fusion method is only simple to combine multiple features together. The contribution of different characteristics to flower classification is not taken into account, thus affecting the effect of classification, in order to further improve the accuracy of flower image classification. In this paper, a flower image classification method based on cellular automata and feature weighted fusion is proposed. In this paper, a method of extracting flower body area based on cellular automata is presented. Firstly, the preprocessing operation of flower image is carried out. Using SLIC algorithm, it is divided into N small super-pixel points, and the color space and the distance space are compared with the classified edge seeds to obtain a significant map based on background. According to the corresponding rules, the new propagation mechanism of cellular automata is used to obtain the optimized salience graph on the background salient graph. Then, a suitable threshold value of the significant graph is found by using the maximum inter-class variance method. Finally, the white part of the binary image is filled on the basis of the original flower map. The main region of flower was obtained. The experiment was carried out on the 17-flower data set. It is proved that this method is effective. (2) A new method of feature weighted fusion classification based on flower body region is presented. The general feature fusion method is only simple to join several features together. The contribution of different characteristics to flower classification is not taken into account in order to improve the classification accuracy of flower image effectively. In this paper, the color features and local features of the flower body area extracted from the above method are firstly weighted fusion, and then the flower image classification is realized by using SVM. Finally. Experiments were carried out on 13-flower and 102-flower image datasets. The validity of this method is verified. (3) A flower image classification prototype system based on cellular automata and feature weighted fusion is developed. Matlab7.0 and VB6.0 are used as development tools. A flower image classification prototype system based on cellular automata and feature weighted fusion is designed and implemented.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S68;TP391.41

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