基于SVM的图像分类系统设计文档_成勇

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LANZHOU UNIVERSITY OF TECHNOLOGY

毕业设计

题目基于SVM的图象分类系统

学生成勇

学号10240330

专业班级计算机科学与技术3班

指导教师晓旭

学院计算机与通信学院

辩论日期

摘要

支持向量机(SVM)方法是建立在统计学习理论根底之上的，克制了神经网络分类和传统统计分类方法的许多缺点，具有较高的泛化性能。但是，由于支持向量机尚处在开展阶段，很多方面尚不完善，现有成果多局限于理论分析，而应用显得较薄弱，因此研究和完善利用支持向量机进展图像分类对进一步推进支持向量机在图像分析领域的应用具有积极的推动作用。

本文通过支持向量机技术和图像特征提取技术实现了一个图像分类实验系统。文中首先引入了支持向量机概念，对支持向量机做了较全面的介绍；然后，讨论了图像特征的描述和提取方法，对图像的颜色矩特征做了详细的描述，对svm分类也做了详细的说明；最后讨论了由分类结果所表现的一些问题。测试结果说明，利用图像颜色矩特征的分类方法是可行的，并且推断出采用综合特征方法比采用单一特征方法进展分类得到的结果要更令人满意。

关键词：支持向量机图像分类特征提取颜色矩

Abstract

The support vector machine (SVM) method is based on statistical learning theory

foundation, overe the neural network classification and traditional statistical classification method of faults, and has high generalization performance. But, because the support vector machine (SVM) is still in the development stage, many still not perfect, the existing results more limited to the theoretical analysis, and the use of appear more weak and therefore study and improve the use of support vector machines to image classification support vector machine to further advance in the application of image analysis play a positive role in promoting.

In this paper, support vector machine (SVM) technology and image feature extraction technology implements a image classification experiment system. This paper first introduces the concept of support vector machine (SVM), the support vector machine (SVM) made a more prehensive introduction; Then, discussed the image characteristics of description and extraction method, the image color moment features described in detail, also made detailed instructions for the SVM classification; Finally discussed the classification results of some problems. Test results show that using the torque characteristics of the image color classification method is feasible, and deduce the prehensive characteristic method than using single feature method to classify the results are more satisfactory.

Keywords:supportvectormachine image classification feature extractionColor Moment

目录摘要I

AbstractI

第一章前言1

1.1本课题的研究意义1

1.2本论文的目的、容1

1.3开发技术介绍1

1.3.1 SVM技术及其开展简史1

1.3.2 java技术简介2

第二章系统分析3

2.1 系统需求分析3

2.2 系统业务流程分析3

第三章系统总体设计4

3.1 分类系统的构造4

3.2 图像数据库4

3.3 特征提取模块4

3.4 svm分类模块4

第四章系统详细设计6

4.1 特征提取模块6

4.1.1 颜色矩6

4.2 SVM分类模块7

4.2.1 svm的算法简介7

4.2.2 svm的核函数选择8

4.2.3 svm的核函数8

4.2.4 svmtrain的用法8

4.2.5 svmpredict的用法9

第五章系统测试10

5.1 图像数据10