【正文】 點(diǎn)研究以 SVM 算法為代表的監(jiān)督分類算法及以 kmeans 聚類算法為代表的費(fèi)監(jiān)督分類算法，并結(jié)合 Hu 圖像矩不變特征，對圖像進(jìn)行聚類分析及分類。 有監(jiān)督分類方面，本文采用了提取能夠較好的保持圖像的邊緣、形狀等特性的 Hu矩不變特征作為訓(xùn)練特征，分類方法采用了基于聚類的 SVM 算法。最后將待分類圖片輸入即可得到分類結(jié)果。 無監(jiān)督分類方面，本文采用了 kmeans 分類方法。 在算法研究的基礎(chǔ)上，設(shè)計(jì)并實(shí)現(xiàn)了水果圖像智能分析應(yīng)用 系統(tǒng)，具有創(chuàng)建特征值數(shù)據(jù)庫、創(chuàng)建訓(xùn)練網(wǎng)絡(luò)、圖像有監(jiān)督分類和圖像無監(jiān)督分類等功能。 關(guān)鍵詞 SVM kmeans 圖像分類 基于聚類的智能圖像分析算 法畢業(yè)設(shè)計(jì)論文 Intelligent Image Analysis Based on Clustering Algorithm ABSTRACT Intelligent image processing technology has been widely applied in many fields. In monitoring and alarm security system, in natural gene microscope, and in the middle pattern recognition system, intelligent image processing technology has played highly important role. Currently contentbased image recognition and classification of intelligent technology are facing many problems in specific application for accuracy. This paper will describes intelligent image analysis method and algorithm theory, meanwhile bines with the same characteristics of HU image moments, and focuses on the SVM algorithm for classification and supervision of representatives of the costs of supervised classification algorithms. In the theoretical study, verify the application of results based on the use of MatLab algorithm. In the phase of supervising classification, this paper used Hu moments invariant feature as a training feature that can keep the extracted image edge, shape and other characteristics using SVMbased clustering algorithm. After extracting samples? characteristic value, put into SVM?s training work to have training. Finally the input image can be classified by the classification results. This paper concludes the approach to the classification of nonsupervised classification accuracy of the results meanwhile analyzes and discusses the accuracy. This paper used Kmenas classification method in the field of unsupervised classification. After preconfiguring data, put into classified image, and then by calling the classification function, the system will output the results of automatic classification. Based on algorithm, design and implementation of fruit intelligent image analysis application system with a characteristic value database, training work, image supervised classification and image unsupervised classification features. When the image has supervised classification, the SVM classification count classification, the accuracy rate can reach nearly 70%. KEY WORDS SVM kmeans Image classification I 目 錄 第一章 緒論 .............................................................................................................................. 1 智能圖像分析概述 .................................................................................................... 1 課題背景 ............................................................................................................ 1 國內(nèi)外研究現(xiàn)狀 ................................................................................................ 2 聚類分析 .................................................................................................................... 3 課題目標(biāo)及本文研究內(nèi)容 ........................................................................................ 3 預(yù)期目標(biāo) ............................................................................................................ 3 主要研究內(nèi)容 .................................................................................................... 3 系統(tǒng)方案 ............................................................................................................ 4 本文的結(jié)構(gòu) ........................................................................................................ 4 第二章 技術(shù)基礎(chǔ) ...................................................................................................................... 5 圖像特征 .................................................................................................................... 5 圖像分類方法 ............................................................................................................ 5 圖像分類概念 .................................................................................................... 5 圖像分類原理 .................................................................................................... 6 圖像分類方法 .................................................................................................... 6 MatLab 及圖像智能處理工具箱 .............................................................................. 7 第三章 圖像矩不變特征提取 .................................................................................................. 9 圖像矩不變特征介紹 .............................................................................................. 11 圖像矩不變特征提取 .............................................................................................. 12 第四章 分類算法 .................................................................................................................... 14 SVM 分類算法 ........................................................................................................ 14 kmeans 分類算法 ................................................................................................... 16 第五章 基于 MatLab的圖像分析軟件實(shí)現(xiàn) ........................................................................ 19 軟件功能及系統(tǒng)流程 .............................................................................................. 19 關(guān)鍵 函數(shù)詳述 .......................................................................................................... 19 II 圖像灰度化 ...................................................................................................... 19 圖像平滑與圖像銳化 ...................................................................................... 20 中值濾波 .......................................................................................................... 20 圖像銳化 ............................................