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Special Issue "Entropy in Image Analysis"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 20 December 2018

Special Issue Editor

Guest Editor
Dr. Amelia Carolina Sparavigna

Dipartimento Scienza Applicata e Tecnologia, Politecnico di Torino, Italy
Website | E-Mail
Interests: physics; image processing; liquid crystals; solid state physics; history of science; archaeoastronom

Special Issue Information

Dear Colleagues,

Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. This analysis often needs numerical and analytical methods which are highly sophisticated, in particular for those applications in medicine, security, and remote sensing, where the results of the processing may consist of data of vital importance.

As being involved in numerous applications requiring reliable quantitative results, the image analysis has produced a large number of approaches and algorithms, sometimes limited to specific functions in a small range of tasks, sometimes generic enough to be applied to a wide range of tasks. In this framework, a key role can be played by the entropy, in the form of the Shannon entropy or in the form of a generalized entropy, used directly in the processing methods or in the evaluation of the results, to maximize the success of a final decision support system.

Since the active research in image processing is still engaged in the search of methods that are truly comparable to the abilities of human vision capabilities, I solicit your contribution to this Special Issue of the Journal, devoted to the use of entropy in extracting information from images, and in the decision processes related to the image analyses.

Dr. Amelia Carolina Sparavigna
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Image entropy
  • Shannon entropy
  • Tsallis entropy
  • Generalized entropies
  • Image processing
  • Image segmentation
  • Retinex methods
  • Medical imaging
  • Remote sensing
  • Security

Published Papers (5 papers)

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Research

Open AccessArticle Improved Cryptanalysis and Enhancements of an Image Encryption Scheme Using Combined 1D Chaotic Maps
Entropy 2018, 20(11), 843; https://doi.org/10.3390/e20110843
Received: 4 October 2018 / Revised: 29 October 2018 / Accepted: 31 October 2018 / Published: 3 November 2018
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Abstract
This paper presents an improved cryptanalysis of a chaos-based image encryption scheme, which integrated permutation, diffusion, and linear transformation process. It was found that the equivalent key streams and all the unknown parameters of the cryptosystem can be recovered by our chosen-plaintext attack
[...] Read more.
This paper presents an improved cryptanalysis of a chaos-based image encryption scheme, which integrated permutation, diffusion, and linear transformation process. It was found that the equivalent key streams and all the unknown parameters of the cryptosystem can be recovered by our chosen-plaintext attack algorithm. Both a theoretical analysis and an experimental validation are given in detail. Based on the analysis of the defects in the original cryptosystem, an improved color image encryption scheme was further developed. By using an image content–related approach in generating diffusion arrays and the process of interweaving diffusion and confusion, the security of the cryptosystem was enhanced. The experimental results and security analysis demonstrate the security superiority of the improved cryptosystem. Full article
(This article belongs to the Special Issue Entropy in Image Analysis)
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Open AccessArticle Encryption Algorithm of Multiple-Image Using Mixed Image Elements and Two Dimensional Chaotic Economic Map
Entropy 2018, 20(10), 801; https://doi.org/10.3390/e20100801
Received: 15 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 18 October 2018
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Abstract
To enhance the encryption proficiency and encourage the protected transmission of multiple images, the current work introduces an encryption algorithm for multiple images using the combination of mixed image elements (MIES) and a two-dimensional economic map. Firstly, the original images are grouped into
[...] Read more.
To enhance the encryption proficiency and encourage the protected transmission of multiple images, the current work introduces an encryption algorithm for multiple images using the combination of mixed image elements (MIES) and a two-dimensional economic map. Firstly, the original images are grouped into one big image that is split into many pure image elements (PIES); secondly, the logistic map is used to shuffle the PIES; thirdly, it is confused with the sequence produced by the two-dimensional economic map to get MIES; finally, the MIES are gathered into a big encrypted image that is split into many images of the same size as the original images. The proposed algorithm includes a huge number key size space, and this makes the algorithm secure against hackers. Even more, the encryption results obtained by the proposed algorithm outperform existing algorithms in the literature. A comparison between the proposed algorithm and similar algorithms is made. The analysis of the experimental results and the proposed algorithm shows that the proposed algorithm is efficient and secure. Full article
(This article belongs to the Special Issue Entropy in Image Analysis)
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Open AccessArticle Video Summarization for Sign Languages Using the Median of Entropy of Mean Frames Method
Entropy 2018, 20(10), 748; https://doi.org/10.3390/e20100748
Received: 4 September 2018 / Revised: 27 September 2018 / Accepted: 27 September 2018 / Published: 29 September 2018
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Abstract
Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access
[...] Read more.
Multimedia information requires large repositories of audio-video data. Retrieval and delivery of video content is a very time-consuming process and is a great challenge for researchers. An efficient approach for faster browsing of large video collections and more efficient content indexing and access is video summarization. Compression of data through extraction of keyframes is a solution to these challenges. A keyframe is a representative frame of the salient features of the video. The output frames must represent the original video in temporal order. The proposed research presents a method of keyframe extraction using the mean of consecutive k frames of video data. A sliding window of size k / 2 is employed to select the frame that matches the median entropy value of the sliding window. This is called the Median of Entropy of Mean Frames (MME) method. MME is mean-based keyframes selection using the median of the entropy of the sliding window. The method was tested for more than 500 videos of sign language gestures and showed satisfactory results. Full article
(This article belongs to the Special Issue Entropy in Image Analysis)
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Open AccessArticle A New Image Encryption Algorithm Based on Chaos and Secure Hash SHA-256
Entropy 2018, 20(9), 716; https://doi.org/10.3390/e20090716
Received: 23 August 2018 / Revised: 16 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
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Abstract
In order to overcome the difficulty of key management in “one time pad” encryption schemes and also resist the attack of chosen plaintext, a new image encryption algorithm based on chaos and SHA-256 is proposed in this paper. The architecture of confusion and
[...] Read more.
In order to overcome the difficulty of key management in “one time pad” encryption schemes and also resist the attack of chosen plaintext, a new image encryption algorithm based on chaos and SHA-256 is proposed in this paper. The architecture of confusion and diffusion is adopted. Firstly, the surrounding of a plaintext image is surrounded by a sequence generated from the SHA-256 hash value of the plaintext to ensure that each encrypted result is different. Secondly, the image is scrambled according to the random sequence obtained by adding the disturbance term associated with the plaintext to the chaotic sequence. Third, the cyphertext (plaintext) feedback mechanism of the dynamic index in the diffusion stage is adopted, that is, the location index of the cyphertext (plaintext) used for feedback is dynamic. The above measures can ensure that the algorithm can resist chosen plaintext attacks and can overcome the difficulty of key management in “one time pad” encryption scheme. Also, experimental results such as key space analysis, key sensitivity analysis, differential analysis, histograms, information entropy, and correlation coefficients show that the image encryption algorithm is safe and reliable, and has high application potential. Full article
(This article belongs to the Special Issue Entropy in Image Analysis)
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Open AccessArticle An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion
Entropy 2018, 20(8), 577; https://doi.org/10.3390/e20080577
Received: 23 June 2018 / Revised: 30 July 2018 / Accepted: 31 July 2018 / Published: 6 August 2018
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Abstract
With the rapid development of information storage technology and the spread of the Internet, large capacity image databases that contain different contents in the images are generated. It becomes imperative to establish an automatic and efficient image retrieval system. This paper proposes a
[...] Read more.
With the rapid development of information storage technology and the spread of the Internet, large capacity image databases that contain different contents in the images are generated. It becomes imperative to establish an automatic and efficient image retrieval system. This paper proposes a novel adaptive weighting method based on entropy theory and relevance feedback. Firstly, we obtain single feature trust by relevance feedback (supervised) or entropy (unsupervised). Then, we construct a transfer matrix based on trust. Finally, based on the transfer matrix, we get the weight of single feature through several iterations. It has three outstanding advantages: (1) The retrieval system combines the performance of multiple features and has better retrieval accuracy and generalization ability than single feature retrieval system; (2) In each query, the weight of a single feature is updated dynamically with the query image, which makes the retrieval system make full use of the performance of several single features; (3) The method can be applied in two cases: supervised and unsupervised. The experimental results show that our method significantly outperforms the previous approaches. The top 20 retrieval accuracy is 97.09%, 92.85%, and 94.42% on the dataset of Wang, UC Merced Land Use, and RSSCN7, respectively. The Mean Average Precision is 88.45% on the dataset of Holidays. Full article
(This article belongs to the Special Issue Entropy in Image Analysis)
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