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

Yang, Hengfu; Yin, Jianping; Jiang, Mingfang

doi:10.3390/app8020317

Cited by 17 publications

(9 citation statements)

References 37 publications

(38 reference statements)

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“…In addition, Local Binary Pattern (LBP) is a simple effective local texture feature and widely used in the fields of face analysis, image segmentation, image retrieval and image security et al [30]- [36]. Some image hashing algorithms are able to generate hash sequence from LBP features [15], [37]- [39].…”

Section: E Other Image Hashing Methodsmentioning

confidence: 99%

Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

2019

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This paper presents a robust image hashing algorithm that exploits low-rank decomposition and path integral local binary pattern (pi-LBP), referred to LRPL hashing. The proposed algorithm generates a compact binary sequence from a low-rank component of the normalized image as the hash code. Considering the excellent texture structure description ability of pi-LBP features, the new hashing algorithm extracts a feature vector from a low-rank feature matrix with pi-LBP. The pi-LBP feature vector is then encrypted by using the logistic map to produce the final hash sequence. The Hamming distance between the hash sequences is employed to authenticate the tested image. The experimental results demonstrate that the proposed LRPL hashing method has better robustness and discrimination compared with existing hashing algorithms. INDEX TERMSImage hashing, low-rank decomposition, path integral local binary pattern.

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Section: E Other Image Hashing Methodsmentioning

confidence: 99%

Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

2019

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“…Low-rank representation (LRR) [31] seeks to find the lowest rank representation where data can be represented by linear combinations of the basis in a given dictionary. To further enhance LRR, latent low-rank representation (LatLRR) [32,33] is proposed to recover the unobserved data in LRR.…”

Section: Low-rankness Analysismentioning

confidence: 99%

“…Compute the step size τ t , that satisfies the Armijo-Wolfe conditions [33] via the line search along the path J t (τ) defined by (19); 6:…”

Section: : Repeatmentioning

confidence: 99%

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Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

et al. 2020

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As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks.

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“…Therefore, hashing methods can accelerate ANN search procedures and save on storage. Recently, hashing methods have been applied in the area of computer vision and machine learning [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval

et al. 2020

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In recent years, binary coding methods have become increasingly popular for tasks of searching approximate nearest neighbors (ANNs). High-dimensional data can be quantized into binary codes to give an efficient similarity approximation via a Hamming distance. However, most of existing schemes consider the importance of each binary bit as the same and treat training samples at different positions equally, which causes many data pairs to share the same Hamming distance and a larger retrieval loss at the top position. To handle these problems, we propose a novel method dubbed by the top-position-sensitive ordinal-relation-preserving bitwise weight (TORBW) method. The core idea is to penalize data points without preserving an ordinal relation at the top position of a ranking list more than those at the bottom and assign different weight values to their binary bits according to the distribution of query data. Specifically, we design an iterative optimization mechanism to simultaneously learn binary codes and bitwise weights, which makes their learning processes related to each other. When the iterative procedure converges, the binary codes and bitwise weights are effectively adapted to each other. To reduce the training complexity, we relax the discrete constraints of both the binary codes and the indicator function. Furthermore, we pretrain a tensor ordinal graph to decrease the time consumption of computing a relative similarity relationship among data points. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed TORBW method can achieve superior performance over state-of-the-art approaches.

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

Cited by 17 publications

References 37 publications

Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval

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