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.
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.
如何缩小语义鸿沟，提高图像分类系统的性能，是一个得到广泛研究的课题。
The topic on how to bridge the semantic gap effectively and improve the accuracy of image classification has been widely explored.
在语义层次上检索视频内容，可以突破“语义鸿沟”，提高视频内容的利用效率。
Retrieving video content in the semantic level can break "semantic gap" and increase the utilization efficiency of video content.
因此，模型更接近问题领域，减少了涉众所了解的概念和解决方案所用的表示语言之间的语义鸿沟。
Models are therefore much closer to the problem domain, reducing the semantic gap between the concepts that are understood by stakeholders and the language in which the solution is expressed.
接着，本文提出了一种基于特征互补率矩阵的图像分类方法，试图从两个角度减小语义鸿沟。
Then, we propose an image classification method based on feature complement ratio matrix, which tries to bridge the semantic gap from two aspects.
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.
为了缩短介于低层视觉特征与高层语义特征之间的“语义鸿沟”距离，提出了急需解决的两大关键问题。
This paper presented two key problems to shorten "semantic gap" distance between low-level visual features and high-level semantic features.
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。
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.
由于图像数据中普遍存在的“语义鸿沟”问题，传统的基于内容的图像检索技术对于数字图书馆中的图像检索往往力不从心。
Because of the "semantic gap" which is often encountered in the image data, traditional CBIR technology cant deal with the problem of image retrieval in digital libraries sometimes.
由于图像数据中普遍存在的“语义鸿沟”问题，传统的基于内容的图像检索技术对于数字图书馆中的图像检索往往力不从心。
Because of the "semantic gap" which is often encountered in the image data, traditional CBIR technology cant deal with the problem of image retrieval in digital libraries sometimes.
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