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。
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。
The image semantic attributes are defined by the private attributes in DICOM and are used to extent the semantic module in image database.
用DICOM标准中的私有属性定义了医学图像中的语义属性，并以此属性定义扩充了图像数据库的语义模块。
A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in a small set of samples.
提出一种在小样本的情况下，基于多层贝叶斯网络的医学图像语义建模方法。
In order to improve the performance of the image annotation, an image semantic annotation method based on multi-modal relational graph was proposed.
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。
The thesis presents a semantic vector algorithm, builds up the network of image semantic keywords, and realizes the composite retrieval of the image low-level feature and semantics characteristics.
提出了一种语义向量算法，构建了图像语义关键词网络，实现了图像底层视觉特征和语义的复合索引。
Image semantic classification is an important and challenging task in the field of semantic-based image retrieval.
图像语义分类是基于语义的图像检索研究领域中一个重要且有挑战性的课题。
Image semantic classification is an important and challenging task in the field of semantic-based image retrieval.
图像语义分类是基于语义的图像检索研究领域中一个重要且有挑战性的课题。
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