研究了基于贝叶斯理论的相关反馈方法。
Research on Bayesian decision theory based relevance feedback mechanism.
相关反馈方法有许多种，如移动查询向量、修改特征权重、贝叶斯、支持向量、神经网络等。
Such as move query vector, modify the weight of characteristics, Bayesian, SVM, neural and networks.
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.
本文研究内容涉及特征索引方法、视频语义分类方法、相关反馈方法、视频片段的相似度量以及视觉内容特征的表示等问题。
The research contents include high dimension feature vectors index, semantic video classification, relevance feedback, similarity measure of video clips and video content representation.
同时提出了采用相关反馈方法，根据用户对检索结果的相关性评价，客观地获取照片新闻主体的文字描述，使检索结果更接近用户目标。
According to user's relevant valuation to searching result, we suggest using relevance feedback to get keywords of news object objectively and make the searching result close to user's need.
之后讨论了以上方法的相关反馈的实现。
Later discussed the realization of relevant feedback of the above method.
该算法采用本体论和相关反馈技术相结合的方法。
And the method combines ontology technology and relative feedback technology.
另外，系统实现了基于多特征结合的方法进行检索，并利用基于相关反馈的权重调整方法进一步提高检索性能，使检索结果更加符合用户的视觉效果。
In addition, it USES multi-features weight adjusting method to improve the performance of the system, the result of retrieval will satisfy people's visual receptance.
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.
针对如何在图像检索系统中客观地表达用户的感知，提出了一种基于粗糙集和遗传算法的相关反馈图像检索方法。
A novel auto relevance feedback image retrieval method called the outer auto relevance feedback(OARF) is proposed based on the pseudo-relevance feedback technology.
针对如何在图像检索系统中客观地表达用户的感知，提出了一种基于粗糙集和遗传算法的相关反馈图像检索方法。
A novel auto relevance feedback image retrieval method called the outer auto relevance feedback(OARF) is proposed based on the pseudo-relevance feedback technology.
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