Robust Image Hashing Using Radon Transform and Invariant Features

Liu, Yuling; Xin, Guojiang; Xiao, Yong Hao

doi:10.13164/re.2016.0556

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…Cross-Modal Hashing aims to map high-dimension data of different modalities into a common hash code space, in which the heterogeneous data realizes semantic representation and similarity measurement. With the rapid development of deep learning [21,22], deep learning-based cross-modal hashing [25][26][27][28] has made significant progress recently. It can effectively capture high-level information and explore semantic relevance to bridge modality gap [42].…”

Section: Cross-modal Hashingmentioning

confidence: 99%

“…Recently, lots of deep learning-based works are proposed [13,14,[21][22][23][24], which outperform the traditional hand-crafted feature-based approaches. On the other hand, in order to better apply on large-scale multimedia database, many researchers focus on cross-modal hashing method to reduce the search and storage cost [25][26][27][28]. Compact binary hash codes are generated from multimedia instances by deep neural networks, which contains semantic information.…”

Section: Introductionmentioning

confidence: 99%

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TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

et al. 2021

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Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

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Section: Cross-modal Hashingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

et al. 2021

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“…Experimental results show that the proposed image hashing scheme can resist content-preserving operations. Liu et al proposed a robust image hashing method based on radon transform [4]. In this image hashing algorithm, the mapping coefficient matrix of the circular areas surrounding feature points is first obtained by Radon transform, and then the invariant moments are computed from the coefficient matrix.…”

Section: A Transform Based Image Hashingmentioning

confidence: 99%

“…A robust image hashing function produces similar hash values for images with same visual appearance but also is sensitive to content-changing distortions and malicious attacks. Robust image hashing received extensive attention in recent decades [3], [4]. Many image hashing algorithms are proposed and widely used in many fields including image authentication, digital watermarking, image retrieval, image copy detection, image quality assessment, and multimedia forensics [5]- [10].…”

Section: Introductionmentioning

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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“…For example, the Radon transform is used to construct hashes. The characteristic of the Radon transform [12] determines that this scheme can resist small-angle image transformation problems. The discrete Fourier transform (DFT) based on the polar system and random key [13] ensures the security of hash.…”

Section: Introductionmentioning

confidence: 99%