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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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。
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。
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.
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