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.
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 this image description model, image contents could be analyzed and represented through different levels and the transition from low-level features to high-level semantics is thus achieved.
该图像描述模型通过在不同层次上对图像内容进行分析和描述，实现了从低级特征到高级语义的过渡。
Recently, almost all current approaches rely on distance between low-level features for judging semantic similarity, and then understand the content of image.
目前几乎所有的图像分类方法都依赖于用图像底层特征间的距离来度量图像内容的语义相似度，实现对图像内容的理解。
The contents of image include low level visual features and high level semantic in image retrieval based on content.
在基于内容的图像检索中，图像的内容包括图像的低层视觉特征和高层语义。
In this paper, a new medical image retrieval approach based on low level features and semantic features is proposed.
提出了一种将图像本身的低级特征和语义特征描述相结合的医学图像检索方法。
In this paper, a new medical image retrieval approach based on low level features and semantic features is proposed.
提出了一种将图像本身的低级特征和语义特征描述相结合的医学图像检索方法。
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