scholarly journals Robust Image Hashing with Low-Rank Representation and Ring Partition

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhenjun Tang ◽  
Zixuan Yu ◽  
Zhixin Li ◽  
Chunqiang Yu ◽  
Xianquan Zhang
Keyword(s):  
Image Quality Assessment ◽  
Characteristic Curve ◽  
Visual Representation ◽  
Image Authentication ◽  
Saliency Map ◽  
Low Rank ◽  
Image Hashing ◽  
Copy Detection ◽  
Social Event Detection ◽  
Low Rank Representation

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.

Robust Image Hashing With Singular Values Of Quaternion SVD

2019 ◽  
Author(s):  
Zhenjun Tang ◽  
Mengzhu Yu ◽  
Heng Yao ◽  
Hanyun Zhang ◽  
Chunqiang Yu ◽  
...  
Keyword(s):  
Euclidean Distance ◽  
Characteristic Curve ◽  
Singular Values ◽  
Image Authentication ◽  
Image Features ◽  
Multimedia Systems ◽  
Image Hashing ◽  
Copy Detection ◽  
Robust Image ◽  
Robust Image Hashing

Abstract Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance.

Robust Image Hashing with Isomap and Saliency Map for Copy Detection

2021 ◽  
pp. 1-1
Author(s):  
Xiaoping Liang ◽  
Zhenjun Tang ◽  
Jingli Wu ◽  
Zhixin Li ◽  
Xinpeng Zhang
Keyword(s):  
Saliency Map ◽  
Image Hashing ◽  
Copy Detection ◽  
Robust Image ◽  
Robust Image Hashing

scholarly journals Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

2018 ◽  
Vol 8 (2) ◽  
pp. 317 ◽  
Author(s):  
Hengfu Yang ◽  
Jianping Yin ◽  
Mingfang Jiang
Keyword(s):  
Low Rank ◽  
Image Hashing ◽  
Low Rank Representation ◽  
Uniform Lbp

scholarly journals Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function

2020 ◽  
Vol 10 ◽  
Author(s):  
Conghai Lu ◽  
Juan Wang ◽  
Jinxing Liu ◽  
Chunhou Zheng ◽  
Xiangzhen Kong ◽  
...  
Keyword(s):  
Score Function ◽  
Low Rank ◽  
Low Rank Representation ◽  
Regularized Method

Computing Robust Principal Components by A* Search

2018 ◽  
Vol 27 (07) ◽  
pp. 1860013 ◽  
Author(s):  
Swair Shah ◽  
Baokun He ◽  
Crystal Maung ◽  
Haim Schweitzer
Keyword(s):  
State Of The Art ◽  
Principal Component ◽  
Low Rank ◽  
A Algorithm ◽  
Running Time ◽  
Current State ◽  
Dimensionality Reduction Technique ◽  
Related Variant ◽  
Low Rank Representation ◽  
The Cost

Principal Component Analysis (PCA) is a classical dimensionality reduction technique that computes a low rank representation of the data. Recent studies have shown how to compute this low rank representation from most of the data, excluding a small amount of outlier data. We show how to convert this problem into graph search, and describe an algorithm that solves this problem optimally by applying a variant of the A* algorithm to search for the outliers. The results obtained by our algorithm are optimal in terms of accuracy, and are shown to be more accurate than results obtained by the current state-of-the- art algorithms which are shown not to be optimal. This comes at the cost of running time, which is typically slower than the current state of the art. We also describe a related variant of the A* algorithm that runs much faster than the optimal variant and produces a solution that is guaranteed to be near the optimal. This variant is shown experimentally to be more accurate than the current state-of-the-art and has a comparable running time.

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