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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, Archaeoastronomy

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 (2 papers)

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Research

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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