Abstract
Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.
Highlights
With the popularity of the platforms of the social network, such as Facebook and Twitter, more and more digital images are transmitted via the Internet and stored in the cyberspace
We have proposed a novel image hashing with low-rank representation (LRR) and ring partition (RP)
An important contribution is the calculation of the visual representation based on the saliency map determined by spectral residual model (SRM)
Summary
With the popularity of the platforms of the social network, such as Facebook and Twitter, more and more digital images are transmitted via the Internet and stored in the cyberspace. Laradji et al [15] extracted the hash of the color image by using the hypercomplex numbers and quaternion Fourier transform (QFT) This approach has good discrimination, but its robustness against rotation needs to be improved. Tang et al [19] constructed the feature matrix via the DCT (discrete cosine transform) and learned hash code from the DCT-based matrix by local linear embedding This hashing only resists image rotation within 5°. It can be found that many algorithms do not make a good balance between rotation robustness and discrimination Aiming at this problem, we propose a new image hashing based on low-rank representation and ring partition.
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