【作者】 鲜艳明；

【导师】 付燕；

【作者基本信息】 西安科技大学 ， 计算机应用技术， 2011， 硕士

【摘要】 近年来,随着图像数据的爆炸式增长,图像分类在很多领域都已成为一项关键性工作,因此对图像分类方法进行研究具有十分重要的价值和意义。本文围绕图像特征的有效提取和适应于图像分类的分类器设计两个方面对图像分类方法进行了研究,并开发了基于内容的图像分类原型系统。主要研究内容如下:针对单一特征只能描述图像的部分属性,对图像内容描述比较片面,缺少足够的区分信息,从而导致图像分类精度不高的问题,提出了基于多特征结合和支持向量机(Support Vector Machine, SVM)的图像分类方法。该方法首先分别提取图像的环形颜色直方图特征、灰度共生矩阵特征、小波变换特征和边缘方向直方图特征;然后,对所提取的这些单一特征进行合并,从而形成能够更全面的描述图像内容的综合特征,并对该综合特征进行高斯归一化;最后,采用SVM分类器对图像进行分类。实验结果表明该方法的各类图像的平均分类准确率高于基于单一特征的图像分类方法。针对所提取的图像特征中通常含有相当数量的冗余信息,而这些冗余信息又极大的损害学习器的泛化能力,从而导致图像分类精度不高的问题,提出了基于多特征结合和PCA-RBaggSVM(Principal Component Analysis RBaggSVM)的图像分类方法。该方法首先提取能够更全面的描述图像内容的综合特征;然后,对所提取的综合特征进行PCA降维和高斯归一化;最后,采用同时扰动训练集和SVM模型参数的二重扰动方法构造SVM集成分类器,并利用该SVM集成分类器对图像进行分类。实验结果表明与BP神经网络、C4.5和RBaggSVM方法相比,该方法的各类图像的平均分类准确率更高,训练和分类的总耗时更少。基于上述研究结果,设计并实现了一个基于内容的图像分类原型系统。测试结果表明该原型系统运行正确。更多还原

【Abstract】 In recent years, with the explosive increase of image data, image classification has become a key task in many fields, so the study of image classification has great value. This thesis centers on effectively extracting image features and the design of classifiers for image classification. Moreover, a prototype system for image classification based on content is developed. The main contributions of this thesis are as follows:Since the single feature can only present partial contents of the image, which results in the insufficient distinguishing information, then the classification accuracy of image is not high. So here an approach for image classification based on combination of multi-features and support vector machine(SVM) is proposed. In this method, first feature of annular color histogram(ACH), feature of gray level co-occurrence matrix(GLCM), feature of tree-structured wavelet transform(TWT) and feature of edge direction histogram(EDH) are extracted respectively, then the extracted features are combined to form comprehensive features which can describe image content more completely and they are normalized with Gaussian normalization method. Finally, SVM is applied to classify images. Experimental results show that the accuracy of average classification of different kinds of images by this method is higher than that of the method based on single feature.Since a lot of redundant information of the extracted image features leads to the low classification accuracy of image, we introduce an approach for image classification based on combination of multi-features and principal component analysis RBaggSVM (PCA-RBaggSVM). In this method, first comprehensive features which can describe image content more completely are extracted, then their dimensions are reduced with PCA and reduced-dimension features are normalized with Gaussian normalization method. Finally, by manipulating training sets and SVM model parameters, a classifier of SVM ensemble is formed, which is used to classify images. Experimental results indicate that compared with BP Neural Network, C4.5 and RBaggSVM, this method can bring higher accuracy of average classification of different kinds of images and takes less total time in training and classifying of it.On the basis of above researched results, a prototype system for image classification based on content is designed and implemented. Testing results show that it operates correctly.更多还原