相关反馈

基于BP网络的相关反馈在图像检索中的应用

The application of relevance feedback in image retrieval based on BP network

作为一种有效的解决手段，相关反馈（relevancefeedback）技术在基于内容图像检索（ContentBasedImageRetrieval）的研究中得到了深入的发展。

Relevance feedback ( RF ) is used as an effective solution for content-based image retrieval ( CBIR ) .

基于相关反馈技术的Web检索改进研究与实现

A Survey of Web Information Retrieval Improvement Based on Relevance Feedback

RichGetRicher&图像检索中的一种自适应的相关反馈方法

Rich get richer & an adaptive relevance feedback approach for content based image retrieval

基于用户相关反馈的带结构语义的XML查询词扩展

Query Expansion with Structural Semantics Based on Users ' Feedback for XML Documents

综合颜色和纹理及SVM相关反馈的图像检索

Image retrieval based on SVM relevance feedback using color and texture feature

图像检索中一种有效的SVM相关反馈算法

Efficient Relevance Feedback Scheme Based on SVM in Image Retrieval

一种改进的SVM相关反馈图像检索方法

An Improved Image Retrieval Approach Based on Support Vector Machine

基于SVM和粗糙集的图像检索相关反馈技术研究

Research of Relevance Feedback of Image Retrieval Based on SVM and Rough Set

一种基于SVM的相关反馈图像检索算法

A SVM Based Relevance Feedback Algorithm for Image Retrieval

基于贝叶斯分类器的最小欧式距离图像检索相关反馈及DSP实现

A Minimum Euclidian Distance Image Retrieval Relevance Feedback Algorithm Based on the Bayesian Classifier and Implementation on DSP

对向量近似方法中k近邻搜索算法加以改进，应用到基于相关反馈的交互式图像检索系统中。

A new k-nearest neighbor search algorithm based on VA-File for relevance feedback image retrieval is introduced in this paper .

综合目标区域纹理和SVM相关反馈的图像检索方法

The Image Retrieval Approach Based on Texture Features of Object Areas and Support Vector Machine

最后本文介绍了利用MatlabGUI中实现的简单的相关反馈图像检索系统。

Lastly , the paper introduces an simple image retrieval system based on relevance feedback which realizes using the Matlab ' GUI .

截止目前，人们已提出大量的相关反馈技术，其中基于支持向量机（SupportVectorMachines，SVM）理论的相关反馈方法备受关注。

Up to now , lots of relevance feedback algorithms have been proposed , in which support vector machines ( SVM ) based relevance feedback methods have received more concern .

DCD用于相关反馈的自然图像检索

Relevance Feedback-based Natural Image Retrieval Using DCD

结合颜色、纹理、形状和多特征对图像进行相关反馈实验分析，并比较SVM增量相关学习算法。

Combined with color , texture , shape and multiply characteristics of images to analysis SVM relevance feedback , and the experiment also compare the incremental SVM learning related algorithm .

实验验证了方法的有效性。5.提出了基于结合先验知识SVM的相关反馈方法。

The experiments validate the feasibility and validity of the method proposed . 5 . A new relevance feedback method using SVM with priori knowledge is proposed in this paper .

详细介绍了NMF（非负矩阵分解）相关反馈应用到医学图像检索。

Details the applications of nonnegative matrix factorization ( NMF ) relevance feedback to medical image retrieval .

由于相关反馈（RF）技术有效地解决了“语义鸿沟”，成为基于内容的医学图像检索系统中提高检索性能的关键技术。

Relevance feedback ( RF ), a effective approach to bridge " semantic gap " and boost image retrieval , has become a key part of content-based medical image retrieval ( CBMIR ) .

本文对比了前向神经网络中的BP、FP和RBF三种网络学习算法；并在此基础上从机器学习的角度出发，分析了在图像检索中基于这三种网络的不同相关反馈技术。

This paper contrasts BP , FP and RBF in the forward neural network , then in the view of machine learning analyzes different relevance feedback techniques based on three networks .

第三部分介绍了CBIR系统的优化方法，主要包括聚类技术对图像数据库的优化和多特征组合检索对图像检索效果的改善，并介绍了相关反馈技术的基本理论和引入图像检索系统的意义。

The third part introduces the improving methods of the CBIR system , mainly including clustering technology . , multi-feature retrieval , and the relevance feedback technology .

同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K-NN、BP神经网络和支持向量机分类器。

At the same time , we used relevance feedback and machine learning used in image retrieval . K-NN , BP neural network and support vector machine classifiers were used in experiments .

先是利用单类SVM的相关反馈技术来解决音乐检索过程中的语义鸿沟问题，然后再通过多样本选择策略的SVM分类来提高检索性能。

First , the use of single-class SVM to solve the relevance feedback in the process of retrieving music semantic gap problem , and then through a variety of the SVM classifier selection strategy to improve retrieval performance .

提出了一种自适应的相关反馈方法&RichGetRicher（简称RGR），它是贝叶斯理论与相关反馈策略相结合的图像检索方法。

The contributions of the dissertation are as follows : ( 1 ) This dissertation presents a relevance feedback method for image retrieval & Rich Get Richer ( RGR ), which is based on Bayesian inference .

基于支持向量机（SVM）的相关反馈机制被认为是一种可以有效跨越该鸿沟的策略，但这种方法没有利用未标记样本的隐含信息。

The relevance feedback based on support vector machine ( SVM ) is regarded as one of the strategies that can solve this problem effectively . However , the information embedded in unlabeled samples is not exploited in that method .

实验证明：在基于SVM图像库预分类的基础上，采用本文的综合特征提取算法和相关反馈算法进行检索得到的结果能更好的满足用户的要求。

Experiments show that on the basis of pre-classification SVM-based image databases , using the integrated feature extraction algorithms with RF approach produced by this paper gets more improvement on the effect , and can better meet the needs of users .

设计了一个基于内容的图像检索系统，该系统支持基于颜色和纹理特征进行检索，并提供两种相关反馈方式：基于SVM的相关反馈和基于SVM与粗糙集结合的相关反馈。

A content-based image retrieval system is designed . This system supports image retrieval based on color and texture features . The system also supports relevance feedback . Two relevance feedback methods are : SVM-based and SVM combined with rough set-based .

文章针对JPEG压缩图像，结合MPEG-7中建议的颜色布局描述符，提出一种带相关反馈的基于DCT域的快速图像检索方法；

In the paper , a fast image retrieval method based on the DCT domain and correlative feedback is presented , which aims at JPEG images and uses color layout descriptors in the MPEG-7 standard .

该系统采用多Agent协作技术，Meta-Search技术，相关反馈学习算法，以及结合PageRank的期望度启发爬行算法，实现了高效的个性化，主动式在线信息发现。

With the technology of Multi-Agent Cooperation , Meta-Search , machine learning based on relevant feedback , and improved algorithm with PageRank for Agent crawl , the system implements efficient , personalized and initiative online information retrieval .