Formalizing classes of information fusion systems

Kokar, Mieczyslaw M.; Tomasik, Jerzy; Weyman, Jerzy

doi:10.1016/j.inffus.2003.11.001

Cited by 72 publications

(45 citation statements)

References 17 publications

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“…The gain of information fusion is differentiated in [14]: "By combining low level features it is possible to achieve a more abstract or a more precise representation of the world". This difference in the fusion goal is also covered in the Durrant-Whyte classification of information fusion strategies [13], which refers to complementary, cooperative and competitive fusion.…”

Section: General Overview On Information Fusionmentioning

confidence: 99%

“…Based on the theory presented in the last section, in the practice of system design, the following points have to be considered: sensors or sources of information, choice of feature sets, fusion level, fusion strategy and fusion architecture and if further background or domain knowledge can be embedded [14].…”

Section: Information Fusion System Designmentioning

confidence: 99%

“…An exception is presented in [25], where the authors fuse kernel matrices. In [14] the authors proved, with the help of their category theory framework, that classifier and decision fusion are just special cases of data fusion. Furthermore they stated that a correct fusion system is always at least as effective as any of its parts, because, due to fusion several sources, more information about the problem is involved.…”

Section: Information Fusion System Designmentioning

confidence: 99%

“…Fusion has also great impact on other tasks like object recognition, since all objects exist in multi modal spaces. Information fusion has established itself as an independent research area over the last decades, but a general formal theoretical framework to describe information fusion systems is still missing [14]. One reason for this is the vast number of disparate research areas that utilize and describe some form of information fusion in their context of theory.…”

Section: Introductionmentioning

confidence: 99%

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Information Fusion in Multimedia Information Retrieval

Kludas

Bruno

Marchand-Maillet

2008

Lecture Notes in Computer Science

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Abstract. In retrieval, indexing and classification of multimedia data an efficient information fusion of the different modalities is essential for the system's overall performance. Since information fusion, its influence factors and performance improvement boundaries have been lively discussed in the last years in different research communities, we will review their latest findings. They most importantly point out that exploiting the feature's and modality's dependencies will yield to maximal performance. In data analysis and fusion tests with annotated image collections this is undermined.

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Section: General Overview On Information Fusionmentioning

confidence: 99%

Section: Information Fusion System Designmentioning

confidence: 99%

Section: Information Fusion System Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Information Fusion in Multimedia Information Retrieval

Kludas

Bruno

Marchand-Maillet

2008

Lecture Notes in Computer Science

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show abstract

“…Due to the multimodal nature of their input data, multimedia indexing, retrieval and classification systems have in their cores an essential need for information fusion. Over the last decades, the fusion of information with its various application areas has established itself as an independent research area but a general theoretic framework to describe information fusion systems is still missing [9]. The still limited understanding of how fusion works and by what it is influenced is probably a reason why multimedia retrieval is up to today staying behind expectations.…”

Section: Introductionmentioning

confidence: 99%

Exploiting document feature interactions for efficient information fusion in high dimensional spaces

Kludas

Bruno

Marchand-Maillet

2008

2008 First Workshops on Image Processing Theory, Tools and Applications

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Abstract-Information fusion, especially for high dimensional multimedia data, is still an open research problem. In this article, we present a new approach to target this problem. Feature information interaction is an information-theoretic dependence measure that can determine synergy and redundancy between attributes, which then can be exploited with feature selection and construction towards more efficient information fusion. This also leads to improved performances for algorithms that rely on information fusion like multimedia document classification. We show that synergetic and redundant feature pairs require different fusion strategies for optimal exploitation. The approach is compared to classical feature selection strategies based on correlation and mutual information.

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Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

Nsengiyumva

Zhong

Luo

et al. 2021

Structural Contr & Hlth

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In recent years, the demand for reliable and accurate nondestructive evaluation (NDE) of structural systems has been growing and several nondestructive testing (NDT) techniques are being combined to address the issues of uncertainty in the testing results. However, the synergistic combination of multiple NDT techniques and the interpretation of the testing results require the ability to process and analyze data from several sources, a process that is referred to as "data fusion." The latter has received considerable attention since the beginning of the 21st century, thanks to the advances in sensor technologies and data acquisition systems. Also, the new era of "Big Data" where heterogeneous data are measured by different sensors and processed for a unified purpose is another factor contributing to this rapid development and the establishment of new NDT opportunities. This article reviews the recent studies involving NDE data fusion for the inspection of structures and examines the state-of-the-art mathematical expressions used in different fusion algorithms. Challenges in the handling of the NDE data and the application of fusion algorithms are also identified and discussed. A generic framework of applying NDE data fusion is described, and a roadmap for potential research prospects in NDE data fusion is provided.data fusion, diagnostic and prognostic health monitoring (DPHM), multisensor fusion and integration (MFI), nondestructive evaluation (NDE), structural health monitoring (SHM), structural systems | INTRODUCTIONEngineering structures are manufactured through multivariable processes and can easily develop defects during the manufacturing process and suffer from various issues (e.g., inappropriate usages, progressive aging, impact damage, excessive loading, and natural hazards such as earthquakes, hurricanes, etc.) during their service life. 1,2 To this end, several nondestructive evaluation (NDE) techniques have been devised (e.g., ultrasonic testing, infrared thermography, terahertz spectrometry, and imaging) to inspect these structures and their corresponding components to ensure they are free from defects and that their structural integrity is maintained throughout their service life. [1][2][3][4] At the manufacturing stage, NDE determines the structure's fitness to accomplish its intended function, and during the service stage, it determines the structure's health status, monitors the damage mechanism,

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Formalizing classes of information fusion systems

Cited by 72 publications

References 17 publications

Information Fusion in Multimedia Information Retrieval

Information Fusion in Multimedia Information Retrieval

Exploiting document feature interactions for efficient information fusion in high dimensional spaces

Critical insights into the state‐of‐the‐art NDE data fusion techniques for the inspection of structural systems

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