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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 8633

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Special Issue Editors

Dr. Lianmeng Jiao

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Guest Editor

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: information fusion; intelligent computing; machine learning; data mining; target recognition

Dr. Hang Geng

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Guest Editor

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: signal detection and processing; information fusion; state estimation; system modelling

Special Issue Information

Dear Colleagues,

Information fusion is the combination of information from multiple sources that aims to draw more comprehensive, specific, and accurate inferences about the world than that are achievable from the individual sources in isolation. This topic is relevant in many areas: target tracking and recognition in battlefield surveillance, sensor fusion in robotics, image fusion in computer vision, expert opinion fusion in risk analysis, and so forth. Since sensor data are inherently noisy and human experience/knowledge is inevitably imprecise, ambiguous, or irrelevant, the right handling of such uncertain data is always at the core of any fusion system. This gives rise to a series of both theoretical and practical challenges with focuses on two aspects: (1) how the uncertainty is expressed or quantified? and (2) how uncertain pieces of information can be aggregated?

This Special Issue will focus on the latest advances in uncertain information fusion. Possible theories for managing uncertain information include, but are not limited to, information theory, probability theory, Bayesian inference, fuzzy sets, random sets, rough sets, possibility theory, and belief functions. Prospective authors are invited to submit their novel and original manuscripts about the theoretical underpinnings or the practical applications of these theories.

Dr. Lianmeng Jiao
Dr. Hang Geng
Guest Editors

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 submissions that pass pre-check are 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 2600 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

information theory
uncertain modeling
multi-sensor fusion
state estimation
target tracking and recognition
situation assessment
fault detection
image fusion
pattern analysis
data mining

Published Papers (5 papers)

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Research

25 pages, 70134 KiB

Open AccessArticle

Improving Existing Segmentators Performance with Zero-Shot Segmentators

by Loris Nanni, Daniel Fusaro, Carlo Fantozzi and Alberto Pretto

Entropy 2023, 25(11), 1502; https://doi.org/10.3390/e25111502 - 30 Oct 2023

Cited by 4 | Viewed by 1035

Abstract

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the “oracle” method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method. Full article

(This article belongs to the Special Issue Advances in Uncertain Information Fusion)

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23 pages, 2362 KiB

Open AccessArticle

A New Correlation Measure for Belief Functions and Their Application in Data Fusion

by Zhuo Zhang, Hongfei Wang, Jianting Zhang and Wen Jiang

Entropy 2023, 25(6), 925; https://doi.org/10.3390/e25060925 - 12 Jun 2023

Viewed by 833

Abstract

Measuring the correlation between belief functions is an important issue in Dempster–Shafer theory. From the perspective of uncertainty, analyzing the correlation may provide a more comprehensive reference for uncertain information processing. However, existing studies about correlation have not combined it with uncertainty. In order to address the problem, this paper proposes a new correlation measure based on belief entropy and relative entropy, named a belief correlation measure. This measure takes into account the influence of information uncertainty on their relevance, which can provide a more comprehensive measure for quantifying the correlation between belief functions. Meanwhile, the belief correlation measure has the mathematical properties of probabilistic consistency, non-negativity, non-degeneracy, boundedness, orthogonality, and symmetry. Furthermore, based on the belief correlation measure, an information fusion method is proposed. It introduces the objective weight and subjective weight to assess the credibility and usability of belief functions, thus providing a more comprehensive measurement for each piece of evidence. Numerical examples and application cases in multi-source data fusion demonstrate that the proposed method is effective. Full article

(This article belongs to the Special Issue Advances in Uncertain Information Fusion)

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19 pages, 4857 KiB

Open AccessArticle

On the Term Set’s Semantics for Pairwise Comparisons in Fuzzy Linguistic Preference Models

by Ana Nieto-Morote and Francisco Ruz-Vila

Entropy 2023, 25(5), 722; https://doi.org/10.3390/e25050722 - 26 Apr 2023

Viewed by 904

Abstract

The main objective of this paper is the definition of a membership function assignment procedure based on inherent features of linguistic terms to determine their semantics when they are used for preference modelling. For this purpose, we consider what linguists say about concepts such as language complementarity, the influence of context, or the effects of the use of hedges (modifiers) on adverbs meaning. As a result, specificity, entropy and position in the universe of discourse of the functions assigned to each linguistic term are mainly determined by the intrinsic meaning of the hedges concerned. We uphold that the meaning of weakening hedges is linguistically non-inclusive because their semantics are subordinated to the proximity to the indifference meaning, whereas reinforcement hedges are linguistically inclusive. Consequently, the membership function assignment rules are different: fuzzy relational calculus and the horizon shifting model derived from the Alternative Set Theory are used to handle weakening and reinforcement hedges, respectively. The proposed elicitation method provides for the term set semantics, non-uniform distributions of non-symmetrical triangular fuzzy numbers, depending on the number of terms used and the character of the hedges involved. (This article belongs to the section “Information Theory, Probability and Statistics”). Full article

(This article belongs to the Special Issue Advances in Uncertain Information Fusion)

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14 pages, 685 KiB

Open AccessArticle

A Parallel Multi-Modal Factorized Bilinear Pooling Fusion Method Based on the Semi-Tensor Product for Emotion Recognition

by Fen Liu, Jianfeng Chen, Kemeng Li, Weijie Tan, Chang Cai and Muhammad Saad Ayub

Entropy 2022, 24(12), 1836; https://doi.org/10.3390/e24121836 - 16 Dec 2022

Cited by 2 | Viewed by 1397

Abstract

Multi-modal fusion can exploit complementary information from various modalities and improve the accuracy of prediction or classification tasks. In this paper, we propose a parallel, multi-modal, factorized, bilinear pooling method based on a semi-tensor product (STP) for information fusion in emotion recognition. Initially, we apply the STP to factorize a high-dimensional weight matrix into two low-rank factor matrices without dimension matching constraints. Next, we project the multi-modal features to the low-dimensional matrices and perform multiplication based on the STP to capture the rich interactions between the features. Finally, we utilize an STP-pooling method to reduce the dimensionality to get the final features. This method can achieve the information fusion between modalities of different scales and dimensions and avoids data redundancy due to dimension matching. Experimental verification of the proposed method on the emotion-recognition task using the IEMOCAP and CMU-MOSI datasets showed a significant reduction in storage space and recognition time. The results also validate that the proposed method improves the performance and reduces both the training time and the number of parameters. Full article

(This article belongs to the Special Issue Advances in Uncertain Information Fusion)

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12 pages, 603 KiB

Open AccessArticle

Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion

by Yongchuan Tang, Yong Chen and Deyun Zhou

Entropy 2022, 24(11), 1596; https://doi.org/10.3390/e24111596 - 02 Nov 2022

Cited by 35 | Viewed by 3429

Abstract

Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of attention. Both the basic probability assignment (BPA) and the negation of BPA in the evidence theory framework can model and reason uncertain information. However, how to address the uncertainty in the negation information modeled as the negation of BPA is still an open issue. Inspired by the uncertainty measures in Dempster–Shafer evidence theory, a method of measuring the uncertainty in the negation evidence is proposed. The belief entropy named Deng entropy, which has attracted a lot of attention among researchers, is adopted and improved for measuring the uncertainty of negation evidence. The proposed measure is defined based on the negation function of BPA and can quantify the uncertainty of the negation evidence. In addition, an improved method of multi-source information fusion considering uncertainty quantification in the negation evidence with the new measure is proposed. Experimental results on a numerical example and a fault diagnosis problem verify the rationality and effectiveness of the proposed method in measuring and fusing uncertain information. Full article

(This article belongs to the Special Issue Advances in Uncertain Information Fusion)

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