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  本文關鍵詞：植物圖像識別方法研究及實現　出處：《浙江大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 藥用植物 微差圖像 圖像識別 深度學習 卷積神經網絡 特征提取

【摘要】：圖像識別技術目前廣泛應用于傳統(tǒng)制造業(yè)、安保以及互聯(lián)網行業(yè),相關方法都較為成熟。但是,在生物、醫(yī)學以及食品等領域上還有很多空白需要填補,主要在于現在的識別方法往往面向"大"類別識別,例如區(qū)分貓和狗、人和車輛、動物和植物等,并非細粒度(微差)圖像識別的范疇,例如同為菊科下的秋菊和野菊的識別。本文針對微差圖像識別領域中的植物圖像識別進行了方法研究,主要工作如下:1.從圖像底層特征入手,重點研究了 BoV和費雪向量特征編碼方法,提出了基于費雪向量的多特征融合圖像識別方案。實驗表明,在植物圖像識別應用中,基于費雪向量的特征編碼方案具有更好的效果。2.從深度學習方法入手,首先設置對比實驗進行模型初選,研究分析不同訓練模式以及卷積神經網絡深度對植物圖像識別的影響;其次提出了基于選擇性搜索算法的植物圖像關鍵區(qū)域生成方法;最后提出了面向關鍵區(qū)域的基于VGGNet16的植物圖像識別模型,并驗證了本文提出方法的有效性。3.構建植物圖像數據集。數據庫的構建包含兩部分,一是面向圖像識別的公開數據集,用于方法的橫向比較;二是自建的植物領域圖像數據集,并在該數據集的基礎上構建了常用藥用植物圖像集,用于驗證方法的實用性。并將本文所提方法在數據集上進行實驗。4.設計和實現藥用植物圖像識別系統(tǒng)。系統(tǒng)利用本文提出的具有較好效果的方法,在此基礎上,設計了中間結果和最終結果的用戶反饋機制,用以提高系統(tǒng)的圖像識別準確率。
[Abstract]:Image recognition technology has been widely used in traditional manufacturing, security and Internet industries. However, there are still many gaps to be filled in biology, medicine and food. This is mainly due to the fact that current recognition methods are often oriented towards "large" category recognition, such as distinguishing between cats and dogs, humans and vehicles, animals and plants, and is not a category of fine-grained (micro-differential) image recognition. In this paper, the method of plant image recognition in the field of differential image recognition is studied. The main work is as follows: 1. Starting from the bottom features of the image. The method of BoV and Fisher vector feature coding is studied emphatically, and a multi-feature fusion image recognition scheme based on Fisher vector is proposed. The experimental results show that it is applied in plant image recognition. The feature coding scheme based on Fisher vector has a better effect. 2. Starting with the depth learning method, we first set up a comparative experiment to select the model. The effects of different training modes and the depth of convolution neural network on plant image recognition were studied. Secondly, based on the selective search algorithm, the key region generation method of plant image is proposed. Finally, a plant image recognition model based on VGGNet16 for key regions is proposed. The validity of the proposed method is verified. 3. The construction of plant image data set. The construction of database consists of two parts: one is the open data set for image recognition, which is used for the horizontal comparison of methods; Secondly, the image data set of plant domain was built, and the common medicinal plant image set was constructed on the basis of the data set. This method is used to verify the practicability of the method. The method proposed in this paper is tested on the data set. 4. The design and implementation of medicinal plant image recognition system. The system uses the method proposed in this paper with better results. On this basis, the user feedback mechanism of intermediate and final results is designed to improve the accuracy of image recognition.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1403668