轮廓编组可以用来在噪声图像中识别显著结构，在许多高级视觉问题中，如目标识别和基于内容的图像检索等，有很重要的作用。
Contour grouping is used to identify desired structure from noisy image, and very important to many advanced visual problems, such as target recognition and content-based image retrieval.
形状特征提取和表示是基于内容图像检索的重要研究内容之一。
Shape feature extraction and description are one of important research topics in content-based image retrieval.
最后设计出基于内容的图像浏览与检索系统以验证其有效性和正确性，并给出了系统运行效果示例。
Finally, a content-based image browse and retrieval system are designed to certify its validity and correctness, and the demonstrations of systematic operation result are provided.
实验结果表明了该检索算法设计的可行性和对基于内容的图像检索准确率的提高。
Experimental results indicate that algorithm design is feasible and good for the improvement of the accuracy of the retrieving rate to content-based image.
使用区分真实照片与人工图片的算法进行图像的预分类与识别，对于提高基于内容的图像和影片检索的成功率有着较大的现实意义。
Some experiments show that in the image database, which has joined the computer graphics and the real photos together, the content-based image retrieval will lose much of its accuracy.
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.
介绍了基于内容图像检索中多种颜色的特征提取和匹配算法，以及加权欧几里得距离、中心距的加权距离、直方图交集算法等。
Introduces color feature extraction and matching algorithms in content-based image retrieval, such as weighted Euclidean-distance, weighted centre distance, histogram intersection algorithm, etc.
本文介绍了基于内容图像检索的基本原理、检索方式和关键技术，并列举了几种较为先进的图像检索系统。
This paper introduces the basic theory, retrieval mode and critical technology of content-based image retrieval and illustrates some advanced image retrieval systems.
本文介绍了多媒体信息检索的原理与特点，着重阐述了基于内容的图像检索、视频检索和音频检索技术。
This paper introduces the principle and characteristics of multimedia information retrieval and elucidates content-based multimedia retrieval technology of image, video and audio.
基于内容的多媒体检索，包括图像、音频和视频等信息的检索，本文主要是对基于内容的图像检索进行了研究。
Content Based Multimedia retrieval includes the image, audio and video retrieval, among which this paper does the most research on the image retrieval.
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.
在医学信息领域，对医学图像物理特征的研究有助于实现图像的自动分析和基于内容的检索。
In medical informatics, the study of medical images 'physical features contributes to implement auto analysis and content-based retrieval.
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.
本文主要针对基于内容的图像检索中的相关反馈技术展开研究，希望通过本文的工作能够对基于内容图像检索领域的研究和应用有所帮助。
This paper focuses on the relevance feedback techniques in content based image retrieval and try to make this paper helpful for research and application of content-based image retrieval.
最后通过完成一个基于内容的图像检索和浏览的实验系统设计与实现，讨论图像检索的具体实现过程，以及检索中需要用到的技术和可能遇到的问题。
Finally, the technical implementation of our content-based image browse and retrieval test system, as well as some key components in building such a test system, is also covered in great detail.
结合鱼病图像的实际，本文研究并实现了用形状特征、颜色特征和纹理特征分别进行基于内容的图像检索方法。
Considering the fact of the ill fish image, the paper researched and presented the algorithm of content based image retrieval that was according to the represent shape, color or vein in image.
基于内容的图像检索技术和基于语义的图像检索技术正是解决这一问题的有效途径。
Two effective ways has been proposed to solve the problem : one is content-based image retrieval(CBIR) technique which search target images by low-level content feature.
低层视觉特征提取、高维数据索引机制和相关反馈方法是面向大规模图像库基于内容检索的三个关键问题。
Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval.
在基于内容的图像检索系统中，图像低层特征和图像所表达高层概念之间的不一致性导致系统出现语义鸿沟问题。
In content-based image retrieval systems, the inconsistency between image low-level features and the concept of high-level expressed by images lead to system semantic gap problem.
本文对基于内容的图像检索技术原理和关键技术做了研究和分析，特别是对图像的特征提取技术做了深入研究。
In this dissertation, the basic principles and key techniques in the Content-based animation image retrieval technology are researched and analyzed, especially the feature extraction technology.
如何实现特大图像基于内容的检索是航测、遥感和卫星图像等应用中的关键问题之一。
It is one of the key problems for the application of aerial, remote sensing and satellite images how to retrieve super large image quickly.
本文介绍了多媒体信息检索的原理与特点，着重阐述了基于内容的图像检索、视频检索和音频检索技术。
This paper proposes a novel cross-media retrieval approach, which can process multimedia data of different modalities and measure cross-media similarity, such as image-audio similarity.
在基于内容的图像检索基础上，提出了基于高层语义词和颜色词检索。
This paper presents a new image retrieval method based on high-level semantics word and color name.
首先对基于内容图像检索技术的基本原理和框架进行了概要介绍。
Firstly, the basic theory and frame of content-based image retrieval technology are introduced.
有效的高维索引机制是基于内容的图像检索的关键技术，具有重要的理论意义和应用价值。
Efficient indexing schemes for high-dimensional data are important for Content-Based Image Retrieval, with theoretical and applicable value as result.
基于内容的图像检索技术主要利用图像的颜色、纹理和形状特征对图像进行检索，论文重点研究基于颜色和纹理特征的图像检索技术。
The main features used for image retrieval are color, texture and shape. This thesis looks into image retrieval techniques based on color and texture.
在基于内容的图像检索中，图像的内容包括图像的低层视觉特征和高层语义。
The contents of image include low level visual features and high level semantic in image retrieval based on content.
基于内容检索技术中必不可少的关键步骤就是图像特征的提取，其中可提取的特征有颜色、纹理和形状等。
Among the contend-based retrieval technologies, feature extraction is most important. For instance, color, texture and shape feature etc.
由此，基于内容的图像和视频检索技术得到了越来越多的重视，成为了多媒体信息检索和图像处理领域中的重要研究方向。
So, the Content Based image and Video retrieval technology comes into being and become an important research area in multimedia retrieval and image processing.
由此，基于内容的图像和视频检索技术得到了越来越多的重视，成为了多媒体信息检索和图像处理领域中的重要研究方向。
So, the Content Based image and Video retrieval technology comes into being and become an important research area in multimedia retrieval and image processing.
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