视觉显著性特征是模拟生物视觉注意力选择机制的一种具有较好的鲁棒性与不变性的视觉特征。基于视觉显著性特征提出了一种快速的场景配准方法。该方法采用调幅傅里叶变换构造视觉显著性映射；通过对显著特征局部极值特性以及信息丰度的分析，实现显著点的粗定位、预选择与可信度排序；通过图像形态学操作，实现了显著场景区域的生长与合并。在此基础上，提出了SSIFT(saliency scale invariant feature transform）算法，从而减少了场景分类算法的计算量。利用本文方法对美国南加州大学的场景数据库进行测试，实验结果表明这种方法提取的SSIFT特征对于图像的平移、旋转以及光照等变化具有良好的不变性；与经典SIFT算法相比，该方法在计算速度上具有明显的优势，并在识别率上也略优于SIFT算法。

Visual saliency is a type of visual feature simulating visual attention selection mechanism in biological system, and has better robustness and invariance. A rapid scene registration method based on visual saliency is proposed in this paper. Firstly, the method adopts amplitude modulation fourier transform to construct saliency map; then rough location，pre-selection and reliability order of salient points are achieved by analyzing the extremal properties and information richness of saliency feature; morphological operation is used for salient scene region growing and merging on the image. On this basis, SSIFT algorithm is proposed, which can largely reduce the computational cost of scene registration. University of Southern California scene dataset is used to test the presented method, the experimental results indicate that the method has good invariance under image scalings,rotations,translations and lighting variations. Compared with classical SIFT algorithm, the method works at higher speed and has higher recognition rate.