中文摘要：

针对现有利用快速鲁棒特征（SURF）进行图像分类的方法中存在的效率低、正确率低的问题,提出一种利用图像SURF集合的统计特征进行图像分类的方法。该方法将SURF的各个维度及尺度信息视为各自独立的随机变量,并利用拉普拉斯响应区分不同数据。首先,获取图像的SURF向量集合;然后,分维度计算SURF向量集合的一阶中心绝对矩、带权一阶中心绝对矩等统计特征,并构建特征向量;最后,结合支持向量机（SVM）进行图像分类。在Corel 1K图像库上的实验结果表明,该方法查准率较SURF直方图方法和三通道Gabor纹理特征方法分别提高17.6%和5.4%。通过与HSV直方图特征进行高级特征融合,可获得良好的分类性能。与SURF直方图结合HSV直方图方法、三通道Gabor纹理特征结合HSV直方图方法、基于视觉词袋（Bo VW）模型的多示例学习方法相比,查准率分别提高了5.2%,6.8%,3.2%。

英文摘要：

The current method of image classification which uses the Speed Up Robust Feature（ SURF） is low in efficiency and accuracy. To overcome these shortages, this paper proposed an approach for image classification which uses the statistical features of the SURF set. This approach took all dimensions and scale information of the SURF as independent random variables, and split the data with the sign of Laplace response. Firstly, the SURF vector set of the image was got.Then the feature vector was constructed with the first absolute order central moments and weighted first absolute order central moments of each dimision. Finally, the Support Vector Machine（ SVM） accomplished the image classification process with this vector. The experimental results show that the precision of this approach is better than that of the methods of SURF histogram and 3-channel-Gabor texture features by increases of 17. 6% and 5. 4% respectively. By combining this approach with the HSV histogram, a high-level feature fusion method was got, and good classification performance was obtained.Compared with the fused method of the SURF histogram and HSV histogram, the fused method of 3-channel-Gabor texture features and HSV histogram, and the multiple-instance-learning method based on the model of Bag of Visual Word（ Bo VW）,the fused method of this approach and HSV histogram has better precision with the increases of 5. 2%, 6. 8% and 3. 2%respectively.