![]() ![]() The requirement of robustness is that the image hashing algorithm must map visually similar images to the same or similar hashes no matter whether their bit representations are the same or not. The most important properties of the image hashing algorithm are robustness and discrimination. In this paper, we study a novel hashing algorithm based on the low-rank representation model and ring partition. As the hash is a short representation, the use of image hashing can achieve efficient data processing. In practice, the hash is used to represent the input image. It can not only be applied to social event detection ,but also can be used in many other applications, e.g., image retrieval, image authentication, image copy detection, and image quality assessment. In recent years, a useful technique called image hashing attracts much attention of the community of multimedia security, which can extract a short code called hash based on the visual content of the input image regardless of its detailed bits. It is an important task to find a hot event of the social network by detecting image copies. Consequently, there are many image copies of a hot event in the cyberspace. ![]() These forwarded images may undergo some digital operations, such as compression and enhancement. For example, when an important event happens, such as an opening ceremony of the Olympic Games, many people would like to forward the same image of the event in their social network. Therefore, efficient techniques are required for processing massive images and protecting content security. 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. 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. Extensive experiments are done to validate effectiveness of the proposed hashing. Hash similarity is finally determined by norm. 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. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. ![]() This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. Image hashing has attracted much attention of the community of multimedia security in the past years. ![]()
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