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.
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.
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.
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.
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.
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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