为国画图像语义分类的实现奠定了基础。
That lays the foundation for the achievement of TCP images semantic classification.
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.
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.
为服装图像情感语义分类的实现奠定了基础。
That lays the foundation for the achievement of clothing image emotional semantic classification.
提出一种基于对象语义的图像分割和分类方法。
This paper proposes a novel approach to object semanteme based image segmentation and classification.
结果表明，可以在具有较好分类正确率的情况下，使图像具有更全面的语义表示。
The results show that the proposed method can make the image expressed by more comprehensive semantic in the case of getting good classification accuracy.
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.
图像情感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 emotion semantic classification is an important and challenging task in the field of semantic-based image retrieval.
图像中蕴涵着一定的语义，根据图像所表示的语义将其进行分类是非常必要的。
The image contains abundant semantics, it is necessary to classify these images according to semantics which expressed.
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。
Recently, almost all current approaches rely on distance between low-level features for judging semantic similarity, and then understand the content of image.
本文给出了一个基于语义分类的图像检索框架，重点讨论了图像语义归类、图像相似性匹配等问题。
This paper presents a framework of image retrieval based on semantic classification, and the emphasis is laid on semantic classification and the similarity match of image.
文中分析了图像语义标注的现状以及存在的问题，提出了基于语义分类的文物语义标注方法。
The characteristics lie in the aspects of semantic types, morpheme types and whether the compounds are used as independent sentences.
文中分析了图像语义标注的现状以及存在的问题，提出了基于语义分类的文物语义标注方法。
The characteristics lie in the aspects of semantic types, morpheme types and whether the compounds are used as independent sentences.
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