以图像的视觉语义为基础,设计了一种新的空间转换模型,提出了一种新的图像语义描述方法.首先,采用NCut方法对图像进行分割,提取每个区域的颜色、纹理与形状等视觉特征;再用K-Means聚类方法对训练集中所有的视觉特征进行聚类,称聚类中心为视觉语义（Visual Semantic,VS）,用来构造投影空间;然后通过所定义的非线性函数,将每幅图像向投影空间作映射,得到图像的投影特征;最后,为了提高分类器的训练效率与性能,先采用RS（粗糙集）方法对投影特征进行属性约简,再用支持向量机（SVM）进行学习和分类.基于Corel图像集的对比实验结果表明,该方法性能受聚类数的影响不大,鲁棒性强,且性能优于其它方法.

Based on the visual semantics of images,a novel space-transforming model is designed,and a new semantics description method is proposed.In the investigation,first,the method of Normalized Cut（NCut） is used to segment each image into several regions,and such visual features as color,texture and shape,etc.of each region are extracted.Next,all the visual features in the training set are clustered by using the K-Means method,and each cluster center is regarded as a ＂visual semantic＂ to construct a projection space.Then,a nonlinear function is defined to map each image into a point in the projection space.Thus,all the projection features of the image are obtained.Moreover,in order to improve the training efficiency and performance of classifiers,projection features are reduced in attributes via the rough set（RS） method,and are trained and classified by the support vector machine.The results are finally compared with those based on the Corel image set.It is found that the proposed method is robust to cluster number,and is superior to the other methods in terms of retrieval performance.