NDLI: A new SIFT keypoint descriptor for copy detection

Content Provider	IEEE Xplore Digital Library
Author	Yanrong Min Xiaoqiang Li Yunhua Zhang Yangyang Zhao Huicheng Lian
Copyright Year	2011
Description	Author affiliation: School of Computer Engineering and Science, Shanghai University, China (Yanrong Min; Xiaoqiang Li; Yunhua Zhang; Yangyang Zhao; Huicheng Lian)
Abstract	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 keypoint's 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.
Starting Page	842
Ending Page	845
File Size	442919
Page Count	4
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6100280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Scale Invariant Features Transform (SIFT) Noise Spatial coherence Feature extraction Robustness Vectors Arrays spatial coherent feature (SCF) image copy detection
Content Type	Text
Resource Type	Article

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