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.
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.
为解决相似案例差异性问题，提出了基于划分聚类和模糊神经网络的设计案例相似性检索方法。
To improve the diversity of similar cases, similarity retrieval method based on partitioned clustering and General Fuzzy Min-Max neural network is developed.
建立了基于实例的产品配置模型，提出了面向配置的客户需求获取、实例的相似性检索算法以及配置求解过程。
This paper establishes the case-based product configuration model, gives out the acquirement of clients' requirement, the algorithm of search cases and the process of product configuration's method.
实验结果显示，关键维能够很好地提高索引的相似性检索性能，对于加速基于内容的多媒体信息检索具有很大的意义。
Experimental results show that key dimension can be used to improve the performance of index, which is of great significance for accelerating the content based similarity search.
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.
第一种，基于内容文本的检索，和item相关的内容，特别是文字，可用来计算相似性。
For the first, content-based analysis, content associated with the item, especially text, is used to compute similarity.
图象内容的检索需要确定图象之间的相似性。
Contentbased image retrieval needs to determine image similarity.
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.
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.
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.
大容量多媒体数据库的基于内容相似性的检索本质上是高维特征空间中一定距离函数的K近邻问题。
Searches based on content similarities in large multimedia libraries are essentially K nearest neighbor searches in high dimensional Spaces.
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.
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.
模型相似性评价技术在面向制造设计、净成形加工，以及相似工艺检索等个领域有重要的作用。
The technique of model similarity assessment is very important in a lot of fields, such as:DFM(Design for Manufacture), Net-shape Maufacturing, Similarity Technology Indexing, etc.
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.
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.
纹理相似性研究是基于内容检索研究中的一个重要组成部分。
The evaluation of texture similarity is very important in content-based image retrieval systems.
三维模型几何相似性比较算法是基于内容的三维模型检索系统的一个主要研究方向。
The method for geometrical shape similarity matching among 3D models is one of main research problems of 3D model retrieval system.
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.
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.
讨论了检索文档集和检索请求的相似性计算，从而找到与检索请求匹配的文档。
Similarity between query and documents was obtained through computing the similarity between weigh terms of documents and query, and most matching documents were provided.
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.
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.
其中模糊集理论被用来进行相似性匹配从而降低噪音在案例检索时的干扰，SCBR技术用来实施对案例的修改和重用。
Fuzzy set theory was employed to perform approximate matching and thus to reduce potential noise in case retrieval, and the SCBR technology was used to implement the adaptation and reuse for case.
用加权欧氏距离进行相似性度量以提高检索性能。
Weighted Euclidean distance was used to improve retrieval efficiency.
这种检索摈弃了常规数据库检索中的精确匹配方法，通过采用相似性匹配的方法获得检索结果。
The rejection of the conventional database search to retrieve the exact match method, the method used to obtain similar matching search results.
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.
文章对英国《世界纺织文摘》和美国《纺织技术文摘》这两检索工具的相似性和各自的特点作了比较。
The British publication World Textile Abstracts and the American publication Textile Technology Digest are investigsted and compared.
文章对英国《世界纺织文摘》和美国《纺织技术文摘》这两检索工具的相似性和各自的特点作了比较。
The British publication World Textile Abstracts and the American publication Textile Technology Digest are investigsted and compared.
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