An image similarity measure based on joint histogram — Entropy for face recognition

Al-Janabi, M.; Shnain, Noor Abdalrazak; Lu, Shan

doi:10.1109/compcomm.2017.8322815

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Many challenges have been considered in CMSC measure such as mean shift, contrast stretching, additive noise, multiplicative noise, impulsive noise, and blurring. (Aljanabi, Shnain, & Lu, 2017) introduced THS image similarity metric based on information theory. THS method based on Taneja entropy and the alternative of histogram; THS tested on the ORL and Brazilian datasets against structure similarity and feature similarity.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, what distinguishes this proposed measure and makes it unique is when it is implemented to find similarities between images or identify images as in the faces, all these elements are used simultaneously. We have combined all four concepts in one algorithm that can run all these tools and to find the similarities between the images or to recognize the images and thus the results are very accurate and reliable in security and other purposes and the most characteristic feature of the ISSM measure and that makes it unique is the great combination between two basic approaches in image similarity which are statistical approach and information theoretic approach while the previous measures were based on either statistic direction such as the well-known SSIM or based on information theory such as (Aljanabi et al, 2017, and (Aljanabi et al,). ISSM chose the features of the structural similarity index measure due to its performance in such image processing field and entropy as an information theory measure.…”

Section: Joint Histogrammentioning

confidence: 99%

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Design of a hybrid measure for image similarity: a statistical, algebraic, and information-theoretic approach

Al-Janabi

Hussain

Shnain

et al. 2019

European Journal of Remote Sensing

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Image similarity or distortion assessment is fundamental to a wide range of applications throughout the field of image processing and computer vision. Many image similarity measures have been proposed to treat specific types of image distortions. Most of these measures are based on statistical approaches, such as the classic SSIM. In this paper, we present a different approach by interpolating the information theory with the statistic, because the information theory has a high capability to predict the relationship among image intensity values. Our unique hybrid approach incorporates information theory (Shannon entropy) with a statistic (SSIM), as well as a distinctive structural feature provided by edge detection (Canny). Correlative and algebraic structures have also been utilized. This approach combines the best features of Shannon entropy and a joint histogram of the two images under test, and SSIM with edge detection as a structural feature. The proposed method (ISSM) has been tested versus SSIM and FSIM under Gaussian noise, where good results have been obtained even under a wide range of PSNR. Simulation results using the IVC and TID2008 image databases show that the proposed approach outperforms the SSIM and FSIM approaches in similarity and recognition of the image. ARTICLE HISTORY

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Section: Related Workmentioning

confidence: 99%

Section: Joint Histogrammentioning

confidence: 99%

Design of a hybrid measure for image similarity: a statistical, algebraic, and information-theoretic approach

Al-Janabi

Hussain

Shnain

et al. 2019

European Journal of Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The idea of Hill algorithm is to convert cleartext letters into ciphertext letters by a series of linear transformations, the decryption only requires one inverse transformation [11], and the key is the transformation matrix itself. Hill password is a part of the multiple-letter substitution codes and it is also called the matrix transformation password.…”

Section: Improved Hill Encryption Algorithmmentioning

confidence: 99%

The Improved Hill Encryption Algorithm towards the Unmanned Surface Vessel Video Monitoring System Based on Internet of Things Technology

Yang

Lai

Dong

et al. 2018

Wireless Communications and Mobile Computing

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Depending on the actual demand of maritime security, this paper analyzes the specific requirements of video encryption algorithm for maritime monitoring system. Based on the technology of Internet of things, the intelligent monitoring system of unmanned surface vessels (USV) is designed and realized, and the security technology and network technology of the Internet of things are adopted. The USV are utilized to monitor and collect information on the sea, which is critical to maritime security. Once the video data were captured by pirates and criminals during the transmission, the security of the sea will be affected awfully. The shortcomings of traditional algorithms are as follows: the encryption degree is not high, computing cost is expensive, and video data is intercepted and captured easily during the transmission process. In order to overcome the disadvantages, a novel encryption algorithm, i.e., the improved Hill encryption algorithm, is proposed to deal with the security problems of the unmanned video monitoring system in this paper. Specifically, the Hill algorithm of classical cryptography is transplanted into image encryption, using an invertible matrix as the key to realize the encryption of image matrix. The improved Hill encryption algorithm combines with the process of video compression and regulates the parameters of the encryption process according to the content of the video image and overcomes the disadvantages that exist in the traditional encryption algorithm and decreases the computation time of the inverse matrix so that the comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm.

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“…A New likeness measure based on 'Affinity Propagation' introduced in 2018 [9]. As well, in 2018 introduced likeness measure based on "joint histogram-Entropy" [10]. In 2019, intrduoced ahybrid measure for similarity of images [11].…”

Section: Introductionmentioning

confidence: 99%

A hybrid image similarity measure based on a new combination of different similarity techniques

Hamza¹,

Dihin²,

Abdulameer³

2020

IJECE

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Image similarity is the degree of how two images are similar or dissimilar. It computes the similarity degree between the intensity patterns in images. A new image similarity measure named (HFEMM) is proposed in this paper. The HFEMM is composed of two phases. Phase 1, a modified histogram similarity measure (HSSIM) is merged with feature similarity measure (FSIM) to get a new measure called (HFM). In phase 2, the resulted (HFM) is merged with error measure (EMM) in order to get a new similarity measure, which is named (HFEMM). Different kindes of noises for example Gaussian, Uniform, and salt & ppepper noiser are used with the proposed methods. One of the human face databases (AT&T) is used in the experiments and random images are used as well. For the evaluation, the similarity percentage under peakk signal to noise ratio (PSNR) is usedd. To show the effectiveness of the proposed measure, a comparision anong different similar technique such as SSIM, HFM, EMM and HFEMM are considered. The proposed HFEMM achieved higher similarity result when PSNR was low compared to the other methods.

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An image similarity measure based on joint histogram — Entropy for face recognition

Cited by 8 publications

References 6 publications

Design of a hybrid measure for image similarity: a statistical, algebraic, and information-theoretic approach

Design of a hybrid measure for image similarity: a statistical, algebraic, and information-theoretic approach

The Improved Hill Encryption Algorithm towards the Unmanned Surface Vessel Video Monitoring System Based on Internet of Things Technology

A hybrid image similarity measure based on a new combination of different similarity techniques

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