实验表明本文提出的图像颜色特征提取算法可成功应用于海量图像库检索和图像语义信息的自动提取。
Experiments show that the new algorithm proposed can be successfully used in retrieving the image from good-sized image database and extracting semantic information from image automatically.
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。
A novel automatic image annotation approach is proposed to bridge the semantic gap of content-based image 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。
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.
本文对基于区域语义和底层视觉特征结合的相关反馈图像检索技术进行了探讨。
This paper do some research on region and vision feature based image retrieval with relative feedback.
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。
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.
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。
It is a significant and challenging issue to utilize relevant feedback of users effectively to implement the semantic-based image retrieval.
在基于内容的图像检索基础上，提出了基于高层语义词和颜色词检索。
This paper presents a new image retrieval method based on high-level semantics word and color name.
提出一种结合图像分块纹理特征和语义信息的医学胸片图像检索方法。
This paper presented a method of medical images retrieval about sternums based on texture features combining with semantic information.
基于概念分布进行检索是实现图像语义检索的方法之一。
This paper proposes a method of image semantic annotation and retrieval based on concept distribution.
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。
The method to LSI based image retrieval is discussed in detail, and the corresponding algorithms are given.
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。
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.
在检索结果的基础上，采用基于实例的方法实现了对图像语义的自动标注。
At last, on the basis of retrieval results, an example-based method is introduced to annotate images automatically.
只有结合图像的多种信息，特别是语义信息，才能使检索系统的能力尽可能接近人的理解水平。
None but combine the multi-character, especially semantic information, can the capability of retrieval system approach the human mentally level.
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。
The key point of the semantic-based image retrieval is the semantic-based image annotation.
基于内容的图像检索技术和基于语义的图像检索技术正是解决这一问题的有效途径。
Two effective ways has been proposed to solve the problem : one is content-based image retrieval(CBIR) technique which search target images by low-level content feature.
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。
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.
图片自动语义标注是基于内容图像检索中很重要且很有挑战性的工作。
Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in content-based image retrieval.
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.
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 experimental results show that the image annotation tool improves the performance of semantic retrieval substantially.
该系统利用了一个多级图像描述模型将语义特征结合到图像检索技术中。
In this paper, a novel system for content-based image retrieval is designed and created, which combines image semantics based on a multi-level model for image description.
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。
In the animation image retrieval system, image semantic annotation has a directly influence on the result of 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 emotion 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。
Image semantic classification is an important and challenging task in the field of semantic-based image retrieval.
用蚁群算法的思想，利用用户的反馈信息建立图像的语义网络，并依据该语义网络用迭代的方法来检索图像。
It establishes a semantic network of images according to users' relevant feedback based on the ant colony algorithm, and then retrieves images by using the semantic network iteratively.
图像语义是研究图像模式识别与图像检索的一个新理论。
同时提出一种基于内容的图像标引与检索系统结构，能自适应的在图像语义库中添加较为成功的语义表述。
The same time, it raise a new structure of system of content based image indexing and retrieval which can adapt oneself for adding successful semantic users did to semantic database.
本文通过对现有基于内容图像标引及检索技术的简要介绍，提出应在现有系统中增加图像的高层语义概念描述，以更接近于人的视觉效果。
This paper introduced the technology of content based image indexing and retrieval concisely. It propose to increase high level semantic describe of image to approach visual sense of human being.
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。
In this paper, a new medical image retrieval approach based on low level features and semantic features is proposed.
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。
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。
The contents of image include low level visual features and high level semantic in image retrieval based on content.
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