通过对自然图像库的实验结果表明，该方法在相似图像检索中具有更好的性能。
Experiment results on natural images show good retrieval quality based on the semantic similarity measure method.
首先叙述了基于内容的图像检索的系统模型和特点，接着针对颜色、纹理和形状进行了概率特征提取、相似度量等的进一步具体分析讨论。
It presents the model and feature of content-based image retrieval system, and then discusses some methods of feature abstraction and similarity measurement based on color, texture and shape.
纹理是图像的重要属性，基于纹理特征检索图像是当前的研究热点，对图像的纹理进行相似性比较是进行图像检索的关键。
Texture is an important item of image information, texture-based image retrieval has been an active research area, and the similarity comparison of texture features is a key to image retrieval.
传统CBIR技术试图通过分析图像视觉特征的相似性来检索图像，这不能满足普通人按语义检索图像的需求。
Traditional techniques of CBIR try to retrieve images through analyzing the similarity of image visual features, but CBIR cannot meet the requirements of semantic image retrieval.
在检索中，颜色和纹理特征的权重不同，本文采用线性加权方式综合颜色特征相似距离和纹理特征相似距离，对图像进行检索。
In this paper, we using a kind of comprehensive image retrieval which fuses color and texture features by linear weights and discuss the method which the weights are determined.
基于内容的图像检索是根据描述图像视觉内容的特征向量进行相似性检索，其中图像视觉内容的提取可以是通用的，也可以是基于特定领域的。
Content based image retrieval is to perform the similarity retrieval according to the image features representing the image content, which may be extracted in the generic or specific domain.
本文提出了以信任度、可能性测度、权重有隶属函数概念作为模糊相似性匹配的基础来检索图像的方法。
In this paper, we put forward the concept of belief measure, possible measure, membership function and weight as the base of fuzzy similarity match, with these functions we can retrieve images.
避开图像相似度大小的定义，通过决策表理论解决图像的分类与检索问题。
To avoid the similarity definition of images, classification and searching problems of images are solved through decision-making table theory.
基于内容的图像检索(CBIR)技术的研究主要包括两个方面：可视化特征提取和相似性度量。
The principal research of content based image retrieve (CBIR) includes two aspects: visual feature representation and similarity measurement.
在基于图像内容的图像检索系统中，搜索引擎检索图像类似于按照相似标准来查询图像。
In content based image retrieval system, search engine retrieves the images similar standard to the Cey words query image according to a similarity measure.
文中介绍了一个基于内容的图像检索系统的设计和实现，它利用改进的几何散列技术能够获得快速而且准确的相似形状检索。
This paper presents the design and implementation of a content-based image retrieval system which acquires effective and efficient similar shape retrieval using a modified geometric hashing technique.
基于内容的图像检索技术依据图像的画面内容特征来检索图像库中与目标图像相似的图像。
The Content-based image retrieval technique search the image in the image library by the content features. The result is similar to the target image.
在此基础上，讨论了图像的相似度量以及相应的图像检索技术，并给出了实验结果和图像检索性能的评价。
And based on this, this paper discusses on the similarity measuring and corresponding image retrieval techniques, and gives the evaluation on the test results and the image retrieval performance.
最简单的检索方法是顺序扫描数据库中的所有图像，计算它们与检索图例的相似性。
The simplest method is sequentially scanning, measuring the similarity between the query example and each image in database.
但是，由于图像的每种特征只能抓住图像相似性的某一个方面，因此如何能更好地表示图像就成为基于内容图像检索中一个重要的研究方向。
But each feature of image can only catch one aspect of the similarity of image, how to represent images better has become a important research field in content-based image retrieval.
面对这种研究现状，本文详细分析了基于内容的图像检索的各种特征提取方法、相似性度量方法以及相关反馈技术等。
According to that, the paper expatiates on key technologies used in CBIR researches, such as feature extracting, similarity measuring, and relevance feedback, etc.
基于内容的多媒体信息检索是当前世界的研究热点，然而在图像内容表示及其相似性度量这两个关键问题上取得的进展还不能令人满意。
Content based image retrieval (CBIR) has been an active research area, however, the achievements in image representation and similarity measurement are not satisfying.
实验表明，该方法能检索出有相同病理特征的相似颅骨图像。
Experiment result proves that this method can retrieve similar skull images with same pathological features.
摘要：提出了利用角检测技术进行图像的相似性检索。
Absrtact: Proposed a new image retrieval method using comer detection.
针对不同特点的图像，融合不同的图像特征，并采用不同的相似性度量方法，提高了图像检索的准确率。
Fusing different image features and using different similarity measures depending on different characteristics improves the accuracy of image retrieval.
得出影响检索效果的关键之处在于图像的内容表示以及图像间的相似性度量。
The key factors that decide the efficiency of an image retrieval system are the description of image content and measure the similarity between two images.
图像数据库容量的增长，迫切需要研究高效的索引技术来支持快速相似性检索的要求。
As the volume of image database grows, it is urged to work over high effective index technique to support fast similarity search in very large databases.
并可检索到具有一定相似性的图像，且类间与类内分形码距离约相差8，类内距离远小于类间距离。
The similarity images can be retrieved and the difference between inter-class and inner-class distance is about 8. The inner-class distance is much smaller than the inter-class distance.
本文给出了一个基于语义分类的图像检索框架，重点讨论了图像语义归类、图像相似性匹配等问题。
This paper presents a framework of image retrieval based on semantic classification, and the emphasis is laid on semantic classification and the similarity match of image.
最后综合利用上述网格区域的颜色直方图和纹理直方图来计算图像间内容的相似度，用于进行彩色图像检索。
Finally, the similarity between color images is computed by using a combined feature index based on the color histogram and texture histogram for local grids.
针对口腔正畸领域中的图像，本文采用一种多特征融合的图像检索方法，医生通过检索出的相似图像找到正畸病患的诊断信息，进而提高诊断效率。
The paper brought up a method based on multi-feature for orthodontics images, doctors could find out the patients' diagnosis information according to some similar images.
本文第四章主要围绕关键帧图像库进行基于关键帧的相似检索问题的研究。
The key frame-based similarity matching measure and retrieval are studied in Chapter 4.
其次，本文研究了图像相似性匹配CBIR关键技术，并实现了本地CBIR检索模块，为图像搜索引擎原型系统构建起了总体框架。
Secondly, based on the key technology of CBIR researching, a local CBIR module is implemented in this thesis, which constructs a general frame for the system of image search engine.
其次，本文研究了图像相似性匹配CBIR关键技术，并实现了本地CBIR检索模块，为图像搜索引擎原型系统构建起了总体框架。
Secondly, based on the key technology of CBIR researching, a local CBIR module is implemented in this thesis, which constructs a general frame for the system of image search engine.
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