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。
In Chapter 4, we propose a novel algorithm for 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。
This paper proposed an automatic image annotation method based on the weighted feature.
提出了一种基于加权特征的图像自动标注方法。
An image annotation method based on mutual information and constrained clustering is proposed.
提出一种基于互信息约束聚类的图像标注算法。
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。
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。
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。
Experiments conducted on standard dataset demonstrate the effectiveness and efficiency of the proposed approach for image annotation.
通过在通用图像集上的实验结果表明，本文提出的自动图像标注方法是有效的。
Concerning the imbalance of the data set used in image annotation, a new self-balancing model based on external database was proposed.
针对图像标注中所使用数据集存在的数据不平衡问题，提出一种新的基于外部数据库的自动平衡模型。
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。
And then, taking into account the specific process of automatic image annotation, we built the automatic image annotation model based on Gaussian mixture model.
再针对具体的自动标注过程，建立了基于高斯混合模型的自动图像标注模型。
Image annotation plays a very important role in managing and retrieving abundant images over the Internet nowadays.
目前图像标注技术在整理、分类、组织和检索互联网图像中扮演着重要的角色。
Image annotation plays a very important role in managing and retrieving abundant images over the Internet nowadays.
目前图像标注技术在整理、分类、组织和检索互联网图像中扮演着重要的角色。
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