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基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等
相似度：马氏距离，欧氏距离等
相关反馈：机器学习方法，如SVM，神经网络等
检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大-content-based image retrieval of some of the key issues : Feature Extraction : color histogram; Texture characteristics of simila
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一种特征库可更新的相关反馈图像检索方法，写的比较详细做图像检索反馈时可以参考-a feature library can be updated relevance feedback image retrieval methods, write more detailed feedback so images can be indexed reference
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一种基于svm相关反馈的图象检索算法-svm based relevance feedback algorithm for image retrieval
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一个基于内容的图像检索程序，用于相关反馈检索，采用自己研制（已发表）的查询点学习算法。-a content-based image retrieval procedure for relevance feedback retrieval, own research (published) inquiries point learning algorithm.
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matlab编程，基于颜色和纹理的图像检索算法的研究，思路清晰，方法有效。,matlab programming, based on the color and texture image retrieval algorithm is clearly thought effective.
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The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS: http://en.wikipedia.org/wiki/CBIR). It enables you to do Query By Example (QBE: http://en.wikipedia.org/wiki/QBE) on images, giving you the opportunity to improve
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该代码为基于内容的图像检索系统CBIR中相关反馈的代码，相关反馈可以提高检索的查全查准率-The code is content-based image retrieval system, relevance feedback CBIR in the code, relevance feedback can improve the retrieval recall precision
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基于支持向量机的相关反馈图像检索算法
相关反馈技术是近年来在图像检索中较为重要的
研究方法, 从机器学习的角度, 以支持向量机(SVM ) 为分类器, 提出了一种新的相关反馈方法-Support vector mach ine based relevance
feedback algorithm in image retrieval
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Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval, Relevance Feedback for Content-Based Image Retrieval Using Bayesian Network,CONTENT BASED IMAGE RETRIEVAL,Relevance Feedback,A survey of browsing models for content based
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个人代码，实现了基于内容的图像检索，包括relevance feedback, virtual feature等特性,基于KDE paper: long-term cross-session relevance feedback in image retrieval-Personal code, to achieve a content-based image retrieval, including relevance feedback, virtual feature and other f
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Content-based medical image retrieval is now getting more and more attention in the
world, a feasible and efficient retrieving algorithm for clinical endoscopic images is urgently
required. Methods: Based on the study of single feature image retr
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论文《A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization》的代码-the code of <A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization>
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粒子群优化算法及其在图像检索中相关反馈上的应用-Particle Swarm Optimization and its relevance feedback in image retrieval
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基于图像检索的matlab代码，基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等 相似度：马氏距离，欧氏距离等 相关反馈：机器学习方法，如SVM，神经网络等 检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大-content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of si
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基于内容的图像检索 基于颜色、纹理、形状的图像检索
基于区域的图像检索
基于语义的图像检索
相关反馈
-Based on the content-based image retrieval based on color, texture, shape-based image retrieval region-based image retrieval based on semantic image retrieval relevance feedback
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图像检索相关反馈方面牛人的文章。很有参考价值。-The paper about the relevance feedback of Image retrieval . it is very useful.
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Nowadays, Content-Based Image Retrieval (CBIR) is the
mainstay of image retrieval systems. To understand the query
semantics and users expectations so as to communicate faithful
results in terms of accuracy, Relevance Feedback (RF) was
incorp
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In the current decade, we are witnessing a great interest in Content Based Image Retrieval (CBIR) together with a wealth of promising technologies, paved for a large number of new mechanisms and systems. In terms of mechanisms, a strong trend towards
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Content based image retrieval with relevance feedback tree.
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