SEMANTIC IMAGE DESCRIPTION AND CLASSIFICATION BASED ON GENERALIZED SET

Ri ChangYong, Pak DuHo, Rim KyongChol, Ju JinHok
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Abstract

A semantic image description model based on generalized set is proposed, and the semantic similarity (distance) measure between images is presented. Semantic image information can be completely represented in this model as compared with previous researches based on vector space. The semantic image description model based on generalized set is similar to human understanding of image knowledge. For the purpose of the semantic image classification, semantic distance based on support vector machine classifier is employed. Experimental results show the validity of new method, and that the image classification accuracy is improved.
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基于广义集的语义图像描述与分类
提出了一种基于广义集的语义图像描述模型,并给出了图像之间的语义相似度(距离)测度。与以往基于向量空间的研究相比,该模型可以完整地表示语义图像信息。基于广义集的语义图像描述模型类似于人类对图像知识的理解。为了对语义图像进行分类,采用了基于支持向量机的语义距离分类器。实验结果表明了新方法的有效性,提高了图像的分类精度。
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