Image retrieval algorithms is the core of content-based image retrieval.
基于内容的图像检索的核心就是图像检索算法。
In recent years, the content-based image retrieval (CBIR) system is a hot research topic.
近年来，基于内容的图像检索系统(CBIR)是一个热门的研究话题。
Content Based image Retrieval is an important and fascinating point of the image database.
基于内容的图像检索是当前图像数据库领域的一个研究热点。
The key point of the semantic-based image retrieval is the semantic-based image annotation.
基于语义的图像检索的闭键和难里反在于基于语义的图像本注。
To cure the above problems, this paper presents a novel shape based image retrieval algorithm.
针对以上问题，该文提出了一种新的基于形状的图象检索算法。
The evaluation of texture similarity is very important in content-based image retrieval systems.
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 traditional float vector based image feature is the base of content based image retrieval techniques.
传统的以浮点矢量形式表示的图像特征，是基于内容的图像检索技术的基础。
In this paper, constructing object description model in content based image retrieval system is focused on.
研究了基于内容的图像检索系统中的目标描述模型的建立方法。
The method to LSI based image retrieval is discussed in detail, and the corresponding algorithms are given.
在此基础上，详细讨论了将隐含语义索引技术应用于图像检索中的具体方法，并给出了相应的算法。
In this basis, the relevance feedback technology and relevance feedback model of content-based image retrieval.
在此基础上，引入基于内容的图像相关性反馈技术及相关性反馈模型。
Shape feature extraction and description are one of important research topics in 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。
Image semantic classification is an important and challenging task in the field of semantic-based image retrieval.
图像语义分类是基于语义的图像检索研究领域中一个重要且有挑战性的课题。
In this paper, an approach to color-based image retrieval based on histogram combined with color block is proposed.
提出了一种基于颜色直方图与颜色块相结合的图像检索的新方法。
A novel automatic image annotation approach is proposed to bridge the semantic gap of content-based image retrieval.
针对图像检索中的语义鸿沟问题，提出了一种新颖的自动图像标注方法。
Feature based image retrieval has got more and more attention in multimedia database management and date transmission.
基于特征的图像检索在多媒体数据库管理和多媒体通信传输中得到越来越多的重视。
To access these image databases automatically and on demand requires the system of content-based image retrieval (CBIR).
实现基于内容的图象检索系统的关键问题是实现图象的语义分割。
Image emotion semantic classification is an important and challenging task in the field of semantic-based image retrieval.
图像情感语义分类是基于语义的图像检索研究领域中一个重要且有挑战性的课题。
The preparation of typical coating corrosion samples and common frame of grey histogram-based image retrieval are presented.
提出了典型镀层腐蚀试样的制备及利用灰度直方图来检索腐蚀图像的一般框架。
Therefore, content-based image retrieval techniques have emerged, and it become hot in the field of image retrieval research.
因此，基于内容的图像检索技术就应运而生，并逐渐地成为图像检索领域的研究热点。
Fractal coding has been proved useful for image compression, and it is also proved effective for content-based image retrieval.
分形编码在图像压缩方面取得了很好的效果，同时也能够用于基于内容的图像检索。
Finally, the two new salient point detectors are used for image retrieval and a new region-based image retrieval method is proposed.
最后，将两种新算法所提取特征点应用于图像检索，提出了一种新的基于区域特征的图像检索方法。
The results indicate that the Content-based image retrieval method offers distinct advantages over some other image retrieval methods.
实验结果表明这种基于图象内容的检索方法，较方便和准确地达到了图象检索之目的。
The color spatial distribution density can provide the color spatial distribution information for CBIR(Content Based Image Retrieval).
在基于内容的图像检索中，颜色的空间分布密度能提供颜色在空间的分布信息。
The text-based image retrieval technique utilizes the manual image annotation as image character for retrieval and is a precise matching.
其中基于文本的图像检索方法利用人工对图像进行标注作为检索特征，进行的是精确匹配；
Visional feature extraction, high dimensional indexing mechanism and relevance feedback are three important issues in content-based image retrieval.
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。
This paper presents a color images region growing method based on online learning algorithm, which is used for content-based image retrieval systems.
论文阐述了一种基于在线学习算法的彩色图像区域增长法，用于解决基于内容的图像检索系统。
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.
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。
A new method of describing and matching spatial context is proposed to effectively improve the distinguishability of objects in visual words-based image retrieval.
提出了一种空间上下文描述与匹配方法，有效地提高了基于视觉关键词的图像检索中目标对象的可区分性。
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。
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