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中文摘要：
如何跨越图像低层视觉特征到高层语义特征的“语义鸿沟”已成为语义图像检索问题的关键，首先将待分类图像分成五个区域；然后在提取图像底层特征的基础上，采用基于支持向量机组（SVMS）的方法建立图像低层视觉特征到高层语义特征之间的映射，将一幅图像同时归入一类或几类图像语义。实验结果表明，该方法具有较好的检索查全率和准确率。
英文摘要：
The solution of ＂semantic gap＂ which existence between the low-features and the high-level semantic features had become the key in problems of the semantic image retrieval, First separated the image into five part, then extracted low-level features, used a new approach to establish a link from image low-level feature to high-level semantic based on support vector machines. Finally, the images were classified as one or several classes. The experiment proves that it has obtained the high accuracy.
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