A New SIFT Keypoint Descriptor For Copy Detection

摘要：

In this paper, we propose a novel keypoint descriptor coined Spatial Coherent Feature (SCF) based on Scale Invariant Features Transform (SIFT) and the spatial coherent information. Like SIFT, our descriptors encode the salient aspects of image gradient in the keypoints neighborhood. However, instead of only using 4*4 sample regions to computer orientation histogram, we get descriptor based on eight consecutive neighborhood regions, and each region has different size but approximate numbers of pixel. That is to say, the new descriptor is computed on spatial consecutive regions, and it includes neighborhood information around keypoint. The performance of SCF descriptor is tested for copy detection. Experimental results demonstrated that the novel keypoint descriptor can be robust for some kinds of attacks such as scale, rotation and reduce the error matching because of introducing spatial coherency, compared to SIFT descriptor.

关键词： image copy detection Scale Invariant Features Transform (SIFT) spatial coherent feature (SCF)

作者: Yanrong Min Xiaoqiang Li Yunhua Zhang Yangyang Zhao Huicheng Lian

作者单位: School of Computer Engineering and Science Shanghai University Shanghai, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 857-860

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）