导　　师： 李宗民

学科专业： 081202

授予学位： 硕士

作　　者： ;

机构地区： 中国石油大学华东

摘　　要： 随着互联网以及多媒体技术的高速发展,图像、视频、音频等数据数以万计的增长,如何从大量的信息中快速、有效地检索到所需的内容成为目前迫切需要解决的热点问题。其中,基于内容的图像检索一直是多媒体技术研究的热点,它利用算法自动提取图像的低层特征并建立索引,检索时利用特征匹配算法进行相似性度量,选择相似度值高的图像作为检索结果。但是低层图像特征与高层语义之间的语义鸿沟严重影响了检索的精度。为了解决这个难题,人们常常在检索算法中引入机器学习、相关反馈技术来学习语义概念、缩小语义鸿沟,最终提高检索精度。 支持向量机/(以下简称SVM/)作为比较成熟的一个分类方法在图像分类、检索中得到了广泛的应用,也取得了不错的研究成果。本文重点研究了SVM在图像注释与检索中的应用方法,分析并比较了目前现有工作的优缺点。图像检索本身亦可以看作是一个将图像分为相关图像和不相关图像的分类问题,SVM的基本思想就是通过最大化相关图像和不相关图像之间的分类间隔找到能够将两类图像最大限度正确分开的分类超平面。在图像注释中,SVM主要用作图像分类,根据分类对象的不同,可以实现图像整体层注释和区域层注释。在图像检索中,SVM常用来和相关反馈相结合,通过用户交互不断学习修正分类超平面,从而改善检索效果。 针对基于SVM的相关反馈检索中存在的训练样本少、反馈次数有限等问题,本文提出了一种新的算法结合多个支持向量机与主动学习来增强检索性能。该算法针对不同的特征视图的不同统计属性采用不同的核函数来分别训练SVM。本文同时提出了一个新的计算确信度的方法来评估每个SVM的分类结果,以确保每次都能选取包含信息最丰富/(即确信度最低/)的样本让用户标记;另外,为了尽可能地充分利用未标记样本,在每次检索后都选取确信度最高的一批不相关样本来扩充SVM的训练样本集,从而达到减少反馈次数,使检索结果尽快达到用户需求的目的。 根据本文提出的检索算法,我们开发了一个基于多个SVM和相关反馈的图像检索系统,并设计了多个实验来检验算法有效性,同时与其他算法进行了比较。通过对实验结果客观公正的分析,最终验证了我们的图像检索算法能够充分利用未标记数据,在更少的反馈次数下达到较高的检索精度。 With the rapid development of internet and multimedia technology, images, videos and audios increase dramatically. How to retrieval useful data rapidly and effectively from tons of information is the hotspot of current multimedia technology research. Recently, content based image retrieval system /(CBIR/) becomes the main direction of multimedia information retrieval. CBIR extracts low level image features, calculates the similarity of features and chooses the most similar images as the retrieval results. However, the gap between high-level semantics and low-level image features heavily affects the retrieval performance. There are many approaches trying to solve this problem, such as using the semantic information of the objects in images, using machine learning technology to learn the relevance between low level image features and high level semantic concepts, using relevance feedback to learn user's intention etc. Support vector machine /(SVM/) has been widely used in image classification and image retrieval. Image retrieval can be regarded as a machine learning process, which attempts to train a learner to classify the images in the database as two classes, i.e. positive /(relevant/) or negative /(irrelevant/). The basic idea of SVM is to find the exact hyper-plane to separate two classes and maximize the margin between two classes. This paper mainly studies how SVM is used in image annotation and retrieval, and analyzes the merit and weakness of current work. In image annotation, SVM is used to classify images into different semantic classes which can reach image-level annotation and region-level annotation according to the differences of object classes. In image retrieval, SVM is used to combine relevance feedback to improve retrieval precision by gradually modifying the hyper-plane through user’s feedback. To improve active learning with SVM and use more unlabeled data, we propose a new algorithm learning three SVMs separately on the color, texture and shape features extracted from labeled images with three different kernel functions. Different algorithms are used in the selection of the disagreement and agreement samples from the unlabeled data and calculation of their confidence degrees. The lowest confident disagreement samples are returned to user to label and added to the training data with the highest confident agreement samples. According to the algorithm that we proposed, we design and develop an image retrieval system based on multiple SVMs and relevance feedback. Experiments show our algorithm can reach a good result in a much less rounds of feedback compared to other algorithms.

关 键 词： 图像检索 图像标注 相关反馈 主动学习

分 类 号： [TP391.41]

领　　域： [自动化与计算机技术] [自动化与计算机技术]