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.
为了改善图像标注的性能，提出了一种基于多模态关联图的图像语义标注方法。
In order to improve the performance of the image annotation, an image semantic annotation method based on multi-modal relational graph was proposed.
文中分析了图像语义标注的现状以及存在的问题，提出了基于语义分类的文物语义标注方法。
The characteristics lie in the aspects of semantic types, morpheme types and whether the compounds are used as independent sentences.
如何跨越图像底层特征和高层语义之间的语义鸿沟，使机器自动的实现图像语义标注更是研究的难点。
How to annotate the image semantic automatically in order to across semantic gap between the feature and the high-level semantic of the image is a difficult problem.
该方法克服了传统的基于统计的语义标注方法效率低、准确率低的缺点，有效提高了图像语义标注的准确率和效率。
This method overcomes the limit of the traditional statistical based semantic annotation method. It improved the accuracy and efficiency of image semantic annotation.
本文提出了动画素材图像语义标注模板和标注规范，从对象、事件、场景、空间关系等方面对图像的多级语义进行完善的标注。
In this paper, the animated image semantic annotation template and norm are proposed to improve multi-level semantic annotation from the objects, events, scenes, space relations on the image.
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.
对此提出了一种为图像提供语义标签的标注方法。
An annotation procedure for providing images with semantic labels was proposed.
如何挖掘基于语义的相关模型是当前自动图像标注技术中一项重要而迫切的研究课题。
A popular technology is focused on how to build the semantic relevance model for the task of automatic image annotation.
在第四章中，我们提出了一种图像语义自动标注算法。
In Chapter 4, we propose a novel algorithm for automatic image annotation.
图像语义的标注需要解决图像高层语义和底层特征间存在的语义鸿沟。
The semantic gap between image semantic and visual features will be solved in image annotation.
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.
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.
本文通过分析图像语义自动标注的相关技术，深入探讨和研究了图像视觉特征的提取方法。
This paper has a further exploration and study of visual feature extraction depending on analyzing correlative technology of the automatic annotation.
然后，根据训练集中样本图像的标注情况建立图像区域与语义关键字的关联。
Then, the relationship between clustering regions and semantic concepts is established according to the labeled images in the training set.
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.
实验结果表明，通过该方法能提高图像自动语义标注的准确率。
The experimental results show that the new method can improve the precision of image annotation.
实验结果表明，通过该方法能提高图像自动语义标注的准确率。
The experimental results show that the new method can improve the precision of image annotation.
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