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。
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。
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。
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。
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。
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。
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。
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。
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。
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.
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。
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.
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 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。
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.
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。
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