摘要
近年来，基于内容的图像检索(Content-Based Image Retrieval，CBIR)技术获得了蓬勃的发展。当前，该研究领域所面临的主要困难在于，大多数现存的基于内容的图像检索系统是通过对不同图像进行相似度计算来完成图像检索任务的。这种做法虽然取得了一些成功，但是也存在相当大的局限性。主要问题是图像检索质量的好坏在很大程度上依赖于所使用的图像特征，它与人类根据图像语义来判断图像间是否相似的做法存在着很大的区别。这种差别的存在导致目前的大多数图像检索方法很难取得令人满意的图像检索效果。为了解决这个问题，本文提出了一种基于语义分析的图像检索方法。该方法从学习用户语义的观点出发，通过对用户选择的若干相似图像进行学习，找到隐藏在其中的用户语义，从而据此实现对数据库中相似图像的检索。为了获得用于图像语义学习的图像特征，发展了一种基于视觉感知特性的色彩量化算法和一种改进的JSEG图像分割算法。JSEG算法只利用到了量化色彩的分布信息；对此，改进算法通过增加对色彩和纹理信息的分析以提高图像分割的效果。此外，针对相关研究中缺乏图像概念保存研究的现状，提出了一种基于复杂网络的图像语义概念保存方法，以检索未来相似语义的图像。实验结果表明，基于用户语义学习和基于概念保存学习的图像检索效果是令人满意的。
从以上研究思路出发，本文首先对CBIR研究的起源、发展、研究方向和所面临的问题，以及本文的主要研究内容和创新点做了整体介绍。
随后，本文探讨了图像特征提取问题，提出了一种基于视觉感知特性的色彩量化算法。该算法将图像分成边缘、平滑和纹理区域，并采用对不同区域中的像素赋予不同权重的策略以强化边缘和平滑区域的色彩。实验表明，同前人工作相比，该算法能够自适应地保留视觉重要区域的色彩，并具有计算速度快的特点。
在色彩量化基础上，为了得到图像中的对象等高层语义特征，进一步提出了一种改进的JSEG图像分割算法。该算法采用对色彩和纹理进行分析的方法
Recently, the techniques of content-based image retrieval (CBIR) have been achieved great developments. The most existing systems of CBIR fulfill the tasks of retrieving the similar images through computing the degree of similarity of different images. Though those methods have achieved much success, they all have great limitations. The main difficulty in the state of art is that, the qualities of retrieval results by computers are much dependent on the features of images, and they have great differences with human beings who predict the degree of similarity of images through the image semantics. The differences make the retrieval results through most existing methods unsatisfied. To solve this problem, a novel method is proposed in this dissertation for learning the user semantics, which is got from several similar images selected by the users for retrieving the similar images in the database. To obtain the good features for learning image semantics, the methods of color quantization and image segmentation which is improved from JSEG are proposed. The JSEG algorithm only utilized the color distribution of the quantized image; while, the improved algorithm adds the analysis of color and texture information to improve the result of image segmentation. Because there is lack of researches on semantic concept saving after the good retrieval results which have the good semantic concept hidden in similar images, a method of semantic saving based on the research of complex networks is proposed to facilitate retrieving the future similar semantic images. The experimental results show that the retrieval results of learning user semantics method and of semantic concept saving method are both satisfying.
According to the idea mentioned in the last paragraph, this dissertation firstly introduces the research of CBIR in whole, which is included such as the origin of CBIR, the development, the main problems confronted in CBIR, the research areas and innovations in the dissertation.
引文
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