Fast scene recognition based on saliency region and SURF

Scene recognition is a hot topic in the field of computer vision, a fast scene recognition method based on saliency region and SURF (speeded up robust features) is proposed in this paper. This method adopts PFT (phase fourier transform) to construct saliency map, on the basis the algorithm of top-ranking extreme points selection based neighborhood entropy is used get saliency region information. Finally scene recognition is implemented using SURF of the saliency region. The method effectively improves real-time of scene recognition and the capability of scene analysis. Compared with other scene recognition methods, it has a better invariance in image rotation, scaling, translation and a substantial range of affine distortion, meanwhile having better real-time. The results of experiments with university of Southern California scene database demonstrate that the method performed well in recognition result, computational speed and robustness.