【作者】 刘明霞；

【导师】 孟祥增；

【作者基本信息】 山东师范大学 ， 教育技术学， 2006， 硕士

【摘要】 随着互联网技术向宽带、高速、多媒体方向的发展,它以更具人性化的应用方式为人们提供了大量的可用资源,如文本、图像、视频和音频等。作为网络信息资源的重要组成部分,教育资源在提高教学质量,充分发挥信息化教育的优势方面有着重要作用。在众多类型的信息资源中,图像具有直观、形象、易于理解和信息量大等特点,成为教育资源库的重要组成部分。同网络信息一样,教育资源中的图像数量巨大,种类繁多,加之排列方式错综复杂,这给相应的图像检索带来了困难。近年来,基于内容的图像检索技术(CBIR)有了长足的发展。基于内容的图像检索对图像的内容进行分析,并对其特征进行合理的描述,然后利用图像的内容特征检索图像,使得检索过程更加有效,检索结果更能贴近人的视觉感受。图像的内容包括图像的颜色、纹理、形状等视觉特征和语义特征等。其中,纹理特征是最为显著的视觉特征之一。由于纹理描述和分析的复杂性,在基于内容的图像检索系统中纹理特征还没有得到充分利用。本文主要针对自然纹理进行基于概念的分类,并对纹理分割和基于纹理特征的图像检索进行了研究。论文借用自然语言中描述纹理的概念词,将自然纹理进行了基于概念的分类,共分为十大类别:鱼鳞、颗粒、裂纹、斑纹、条纹、绒毛、波纹、木纹、花纹和乱纹。在此基础上,建立了一个小型的自然纹理图像库。对常用的纹理特征提取方法进行了讨论,分析了小波包方法,给出了利用Gabor滤波器提取纹理特征的详细算法。论文中采用BP神经网络和支持向量机作为分类器,进行了纹理分类实验,获得了较好的实验结果。纹理分割是纹理图像分析的关键步骤,因为只有完成对图像的分割才能提取图像中对象的视觉特征和语义特征。论文讨论了纹理分割的一般方法,对基于统计的灰度共生矩阵方法进行了研究,分析了距离、灰度级和窗口大小对特征提取和分割效果的影响。采用灰度共生矩阵方法提取纹理特征,利用模糊C均值聚类方法进行了纹理分割,取得了比较满意的实验结果。论文讨论了基于内容的图像检索的特点,并利用纹理特征进行了图像检索实验,取得了较好的实验结果。更多还原

【Abstract】 With the development of the Internet, which is progressing in high speed towardswide-band and multi-media, it is providing us with more and more available resources,such as the text, the image, video and audio resources and so on. As an important partof the network information, educational resources play a significant role in theimprovement of the educational quality and the realization of its potentials. Among allkinds of information resources, images are much more concrete and intelligible,delivering generous information. Therefore, it has become one of the most significantparts that constitute the educational resource library.Just like all the other network information, the enormous number, the variety andcomplex sequences of the educational images obstruct the advancement of the imageretrieval to a large extent. In recent years, the content-based image retrieval hasimproved rapidly. It’s mainly based on the content and describes the reasonablefeatures of the images in order to make the retrieval more efficient and adjust topeople’s vision as satisfying as possible. The content of images is made up with color,texture, shape , language features and so on. Among all the above, texture is one ofthe most remarkable features. However, texture features haven’t been made full use ofin the content-based image retrieval yet, which description and analysis are complex.In this thesis, we classify the natural texture into ten classes according to theirconception. In addition, we study the texture segment and texture-based imageretrieval of natural images.We apply the texture conception of natural language to the texture classification,and classify the natural texture into ten classes that are YuLin, Keli, Liewen, Banwen,Tiaowen, Rongmao, Bowen, Muwen, Huawen and Luanwen. Basing on the above, wefound a small image library of natural texture. In the thesis, we discuss the commonmeans of texture feature extraction, analyze the Wavelet Packet, and bring forward aspecific algorithm for Gabor filter. In order to verify the validity of the featureextraction, we adopt the BP network and SVM as the classifier to carry out ourexperiments, which bring us satisfying results.Texture segment is an essential step for texture image analysis. Only after segment,we can extract the visible and language features. Here we mostly study the GrayLevel Co-occurrence Matrix. We research into the influence of the distance, the graylevel and the window on the segment result. In the thesis, we make use of the fuzzy ccluster to classifier different texture, and get satisfactory results, too.Finally, we discuss the features of the content-based image retrieval, carry on atexture-based image retrieval experiment, and get a satisfactory result.The whole test platform is based on the Microsoft Windows 2000 System andAccess database system. Using the Visual C++ 6.0 and Matlab 6.5, we explore anatural texture classification and segment system and a texture-based image retrievalsystem. The experiment results indicate that our algorithm is effective.Our research proves that making use of the texture features in content-basedretrieval can improve the precision of the retrieval to a large extent. Therefore we canlet the network images serve the building of our Education Resource Library.更多还原