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。
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。
In order to improve the performance of the image annotation, an image semantic annotation method based on multi-modal relational graph was proposed.
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。
This method overcomes the limit of the traditional statistical based semantic annotation method. It improved the accuracy and efficiency of image semantic annotation.
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。
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.
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。
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。
A popular technology is focused on how to build the semantic relevance model for the task of automatic image annotation.
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。
The semantic gap between image semantic and visual features will be solved in 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 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 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 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。
There are a small amount of pixel-based semantic image annotation systems which have no problem of adequate semantics learning but are time-consuming of the new image's semantic prediction.
也有少数系统是对像素点级别的特征进行语义学习和预测的，这样虽然可以确保特征的学习足够充分，但是语义的预测过程很耗时。
There are a small amount of pixel-based semantic image annotation systems which have no problem of adequate semantics learning but are time-consuming of the new image's semantic prediction.
也有少数系统是对像素点级别的特征进行语义学习和预测的，这样虽然可以确保特征的学习足够充分，但是语义的预测过程很耗时。
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