关键词: 快速鲁棒特征（SURF）, 全局特征, 随机直方图, 支持向量机, 特征融合

Abstract: SURF（Speeded Up Robust Feature） has excellent description ability for local features, but isn’t strong for describing global features. This paper proposes an image classification algorithm based on the combination of SURF and global features. The SURF vector sets are extracted, and the vector sets are reduced to a single high dimensions feature vector by employing the random histogram algorithm. The HSV（Hue, Saturation, and Value） color histogram is extracted. These two features are classified with SVM（Support Vector Machine）, respectively. The two classification results are integrated by the algorithm of high-level cue integration to get the ultimate classification result. The experimental results demonstrate that the proposed algorithm greatly improves the accuracy of image classification.

Key words: Speeded Up Robust Feature（SURF）, global feature, random histogram, Support Vector Machine（SVM）, feature fusion