About: Information Fusion is an academic journal. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 1566-2535. Over the lifetime, 1416 publications have been published receiving 73238 citations.
Topics: Computer science, Artificial intelligence, Sensor fusion, Image fusion, Wireless sensor network
Papers published on a yearly basis
TL;DR: A comprehensive review of the data fusion state of the art is proposed, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies.
Abstract: There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
Abstract: In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.
TL;DR: This paper reviews the varied attempts to provide a formal explanation of error diversity, including several heuristic and qualitative explanations in the literature, and introduces the idea of implicit and explicit diversity creation methods, and three dimensions along which these may be applied.
Abstract: Ensemble approaches to classication and regression have attracted a great deal of interest in recent years. These methods can be shown both theoretically and empirically to outperform single predictors on a wide range of tasks. One of the elements required for accurate prediction when using an ensemble is recognised to be error \diversity". However, the exact meaning of this concept is not clear from the literature, particularly for classication tasks. In this paper we rst review the varied attempts to provide a formal explanation of error diversity, including several heuristic and qualitative explanations in the literature. For completeness of discussion we include not only the classication literature but also some excerpts of the rather more mature regression literature, which we believe can still provide some insights. We proceed to survey the various techniques used for creating diverse ensembles, and categorise them, forming a preliminary taxonomy of diversity creation methods. As part of this taxonomy we introduce the idea of implicit and explicit diversity creation methods, and three dimensions along which these may be applied. Finally we propose some new directions that may prove fruitful in understanding classication error diversity.
TL;DR: The aim is to reframe the multiresolution-based fusion methodology into a common formalism and to develop a new region-based approach which combines aspects of both object and pixel-level fusion.
Abstract: This paper presents an overview on image fusion techniques using multiresolution decompositions. The aim is twofold: (i) to reframe the multiresolution-based fusion methodology into a common formalism and, within this framework, (ii) to develop a new region-based approach which combines aspects of both object and pixel-level fusion. To this end, we first present a general framework which encompasses most of the existing multiresolution-based fusion schemes and provides freedom to create new ones. Then, we extend this framework to allow a region-based fusion approach. The basic idea is to make a multiresolution segmentation based on all different input images and to use this segmentation to guide the fusion process. Performance assessment is also addressed and future directions and open problems are discussed as well.
TL;DR: A relatively detailed study is presented indicating that the color distortion problem arises from the change of the saturation during the fusion process, and PCA, BT, and WT are evaluated and found to be IHS-like image merging techniques.
Abstract: The intensity-hue-saturation (IHS) method, principal component analysis (PCA), Brovey transform (BT) and wavelet transform (WT) are the contemporary image fusion methods in remote sensing community. However, they often face color distortion problems with fused images. In other words, they are sensitive to the characteristics of the analyzed area. To investigate this color distortion problem, this work presents a relatively detailed study indicating that the color distortion problem arises from the change of the saturation during the fusion process. Meanwhile, PCA, BT, and WT are evaluated and found to be IHS-like image merging techniques. Experimental results for distinct image fusion methods are also demonstrated in this paper.
Related Journals (5)