A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in a small set of samples.
提出一种在小样本的情况下，基于多层贝叶斯网络的医学图像语义建模方法。
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。
Therefore, it is still an unsolved problem about how to integrate with semantic features to achieve better connection between the lower physical features and the image content for efficient retrieval.
因此，如何结合语义特征，使得所抽取的低层物理特征和图像内容所表示的语义特征之间建立良好的联系，实现高效的图像检索，仍是很长一段时间内需要研究解决的问题之一。
Traditional techniques of CBIR try to retrieve images through analyzing the similarity of image visual features, but CBIR cannot meet the requirements of semantic image retrieval.
传统CBIR技术试图通过分析图像视觉特征的相似性来检索图像，这不能满足普通人按语义检索图像的需求。
This paper proposes a method of image semantic annotation and retrieval based on concept distribution.
基于概念分布进行检索是实现图像语义检索的方法之一。
Because of the "semantic gap" which is often encountered in the image data, traditional CBIR technology cant deal with the problem of image retrieval in digital libraries sometimes.
由于图像数据中普遍存在的“语义鸿沟”问题，传统的基于内容的图像检索技术对于数字图书馆中的图像检索往往力不从心。
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.
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。
Extraction of object in an image is a precondition for semantic image retrieval.
图像目标提取是图像语义抽取及其应用的基础。
The thesis presents a semantic vector algorithm, builds up the network of image semantic keywords, and realizes the composite retrieval of the image low-level feature and semantics characteristics.
提出了一种语义向量算法，构建了图像语义关键词网络，实现了图像底层视觉特征和语义的复合索引。
In the animation image retrieval system, image semantic annotation has a directly influence on the result of image retrieval.
在动画素材图像的检索系统中，图像语义标注的质量直接影响了检索的效果。
The experimental results show that the image annotation tool improves the performance of semantic retrieval substantially.
实验表明，本标注工具应用到图像语义检索系统中，大幅度的提高了语义检索的性能。
The contents of image include low level visual features and high level semantic in image retrieval based on content.
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。
In this paper, a new medical image retrieval approach based on low level features and semantic features is proposed.
提出了一种将图像本身的低级特征和语义特征描述相结合的医学图像检索方法。
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。
This paper presents a framework of image retrieval based on semantic classification, and the emphasis is laid on semantic classification and the similarity match of image.
本文给出了一个基于语义分类的图像检索框架，重点讨论了图像语义归类、图像相似性匹配等问题。
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。
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。
It is a significant and challenging issue to utilize relevant feedback of users effectively to implement the semantic-based image retrieval.
如何有效利用用户的相关反馈信息来进行基于语义的图像检索，是一个具有重要意义并且极具挑战性的问题。
This two-level semantic annotation research is of great significance to the semantic-based image retrieval system.
这两个层次的语义标注研究对于动画素材图像语义检索系统的高效运行有着重要意义。
This two-level semantic annotation research is of great significance to the semantic-based image retrieval system.
这两个层次的语义标注研究对于动画素材图像语义检索系统的高效运行有着重要意义。
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