Iterative learning control for image feature extraction with multiple-image blends

Zhang, Yinjun; Li, Yinghui; Su, Jianhuan

doi:10.1186/s13640-018-0336-0

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…Due to the characteristics and importance of digital image and to avoid attacks from any unauthorized people/company/system and so on, many different preventive techniques and approaches have been developed to make data over a network confidential and durable and also storing data in secret [13]. These techniques are based on chaos [14,15], DCT [16,17], GA [18,19], XOR operations [20,21] and Neural Networks (NN) [22,23].…”

Section: Image Encryptionmentioning

confidence: 99%

Görüntü Şifreleme Yöntemlerinin Kapsamlı Bir İncelemesi

TİKEN

Şamlı

2022

Harran Üniversitesi Mühendislik Dergisi

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In today’s technology world, data security has a great importance. Because each data type has its own characteristics, there are various methods of providing this security. The main subject of this study is the security of image data which are more complex structures than text data. Using traditional encryption methods alone to ensure security in image data types can create security weaknesses. For this reason, nowadays, some traditional methods are combined with each other or different methods to encrypt image data. In this study, 131 articles were examined and image encryption methods were classified according to the traditional methods they contain. Studies on both colored and gray level images have been handled together. Finally, the images used in the articles were compared with each other in many ways and the results were presented graphically.

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Section: Image Encryptionmentioning

confidence: 99%

Görüntü Şifreleme Yöntemlerinin Kapsamlı Bir İncelemesi

TİKEN

Şamlı

2022

Harran Üniversitesi Mühendislik Dergisi

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“…The work of Zhang et al [14] have used an iterative learning scheme for extracting hidden information in the form of feature.…”

Section: Related Studiesmentioning

confidence: 99%

A Novel Feature Extraction for Complementing Authentication in Hand-based Biometric

Mahalakshmi¹,

Sheela²

2021

IJACSA

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With an increasing usage of hand-based biometrics in authentication system, there is a need to evolve up with more potential security owing to increasing evolution of threats. The security of the hand authentication system completely depends upon uniqueness and distinct selection of features from hand image which has the properties of robustness, fault tolerance, and simpler implication. Review of existing feature extraction literatures shows more inclination towards sophisticated process as well as it also suffers from various other limitation. Therefore, this manuscript resolves this limitation by presenting a novel model of feature extraction which is carried out in more progressive form and less iterative form, unlike existing approaches. The proposed system achieves its research goal by introducing simplified feature extraction operation via storage, blurring, color space conversion, binary image conversion, the modelling aspect of the study emphasizes on image enhancement along with fuzzification for yielding more efficient result. An experimental study has been carried out using Python considering hand-biometric dataset in order to carry analysis where the outcome shows significant supportability over any palmprint recognition system. The study outcome is compared with most standard implementation of feature extraction to find that proposed system offer better accuracy performance in contrast with existing system.

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“…The comparison algorithms of feature extraction methods are: single dense sampling (baseline), dense sampling + Trajectory Correction (baseline + TC), dense sampling + Concept Dictionary (baseline + CD), Cluster, 26 and MVAD 27 . The comparison algorithm of feature selection adopts SIFT, 28 SURF, 29 LBP, 30 Cluster, and MVAD.…”

Section: Performance Evaluationmentioning

confidence: 99%

Leveraging recommendation systems for improving caching emerging short video in content delivery network

2020

Trans Emerging Tel Tech

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Short video applications have rapidly become one of the most popular online entertainments. It is also one of the most important ways of social networking. However, the request pattern of short videos shows the characteristics of highly dynamic compared to traditional video. First, the access of short video is changing quickly, that is, user shows less preferences. Second, most short videos have no tag descriptions. These result in a significant reduction in caching efficiency of content delivery network (CDN) server, which is used to cache short videos. To address these challenges, this article proposes a recommendation system to optimize short video caching. For lack of tag description, this article proposes event detection method to recognize tags based on improved dense trajectory and concept dictionary video feature extraction. Then the collaborative filtering method is used to generate caching recommendation list for CDN to guide short video caching. Simulation results show that the proposed method can significantly improve the efficiency of short video caching for CDN.

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Iterative learning control for image feature extraction with multiple-image blends

Cited by 14 publications

References 32 publications

Görüntü Şifreleme Yöntemlerinin Kapsamlı Bir İncelemesi

Görüntü Şifreleme Yöntemlerinin Kapsamlı Bir İncelemesi

A Novel Feature Extraction for Complementing Authentication in Hand-based Biometric

Leveraging recommendation systems for improving caching emerging short video in content delivery network

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