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中文摘要：
在基于内容的图像检索中，针对图像的低层可视特征与高层语义特征之间的鸿沟，提出了一种基于支持向量机（SVM）的语义关联方法。通过对图像低层特征的分析，提取了颜色和形状特征向量（221维），将它们作为支持向量机的输入向量，对图像类进行学习，建立图像低层特征与高层语义的关联，并应用于鸟类、花卉、海洋以及建筑物等几个典型的语义类别检索。实验结果表明，该方法可适应于不同用户的图像检索，并提高了检索性能。
英文摘要：
A new method for correlating image low-level feature with high-level semantic based on SVM is proposed, aiming at overcoming the considerable gap between them in the field of content-based image retrieval, Through analyzing the image low-level features, color and shape feature vectors are selected as SVM＇s input vectors. Then make study of image classes to build the correlation from image low-level features to high-level semantics. This semantic correlation method has been used in semantic retrieval, which concerns the following typical semantic categories： birds, flowers, sea and buildings. Experimental results demonstrate that the algorithm can adapt to the various users＇ image retrieval and improve the retrieval function.
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