Article
Computer Science, Information Systems
Shaoning Zeng, Bob Zhang, Jianping Gou, Yong Xu, Wei Huang
Summary: Dictionary-based classification is effective in knowledge discovery from image data, but faces challenges in balancing the number of dictionary atoms with classification performance, as well as the speed decrease on large datasets. The proposed FRDC framework improves robustness by introducing l(2)-norm optimization and solving optimization based on both l(1)- and l(2)-norms in stages, enhancing robustness, simplicity, and speed.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Engineering, Electrical & Electronic
Farshad G. Veshki, Nora Ouzir, Sergiy A. Vorobyov, Esa Ollila
Summary: This paper presents a multimodal image fusion method based on coupled dictionary learning, which effectively preserves texture details and modality-specific information, and achieves excellent performance in both visual and objective evaluations.
Article
Engineering, Multidisciplinary
Haoxuan Zhou, Guangrui Wen, Zhifen Zhang, Xin Huang, Shuzhi Dong
Summary: This paper proposes a novel sparse structure frequency analysis framework based on Dictionary Learning and Sparse Representation Theory, which effectively addresses the issue of extracting fault signals from rolling bearings. By introducing methods such as the K-SVD algorithm and Sparse Frequency Response Spectrum Model, the proposed framework overcomes challenges in fault feature extraction. Additionally, a new global filtering feature extraction algorithm is proposed to obtain relevant fault features masked by noise, demonstrating superior anti-noise and adaptability compared to other state-of-the-art algorithms.
Article
Computer Science, Information Systems
Changpeng Ji, Lina He, Wei Dai
Summary: This paper proposes a new image denoising method based on dictionary learning. By reducing dimensions and constructing learning dictionary, the method solves the problem of image smoothness and fuzzy edge texture after denoising. It achieves better denoising results by using structure clustering algorithm and sparse coding theory.
Article
Geochemistry & Geophysics
Kunhong Li, Zhining Liu, Bin She, Jiandong Liang, Guangmin Hu
Summary: Seismic fades analysis based on prestack data focuses on waveform spatial structure rather than amplitude intensity. A dictionary learning method is developed to eliminate amplitude intensity and sparsely represent seismic data. The method shows stronger tolerance to noise and more accurate seismic facies boundary determination compared to other traditional classification methods.
Article
Computer Science, Artificial Intelligence
Shan Gai
Summary: In this paper, a novel sparse representation model for color image based on reduced biquaternion is proposed, which has the advantages of low computation cost and treating the color image as a whole. Experimental results demonstrate that the proposed model achieves state-of-the-art denoising performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Computer Science, Interdisciplinary Applications
Rongchang Zhao, Hong Li, Xiyao Liu
Summary: Dictionary learning has been proven effective in medical image analysis, particularly in glaucoma diagnosis, with the potential of improving computer-aided diagnosis. The method not only captures distinct glaucoma-related features, but also shares common patterns among all fundus images, showing a high accuracy rate of 92.90% in glaucoma diagnosis.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Sumit Budhiraja, Rajat Sharma, Sunil Agrawal, Balwinder S. Sohi
Summary: An efficient image fusion method based on sparse representation with clustered dictionary is proposed in this paper for infrared and visible images. By enhancing the edge information of visible image using a guided filter and using non-subsampled contourlet transform for fusion, the proposed method is able to outperform other conventional image fusion methods.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Ronghui Miao, Jinlong Wu, Hua Yang, Fenghua Huang
Summary: This paper proposes a sparse dictionary learning method combined with improved LK-SVD algorithm for the rapid and accurate identification of nectarine diseases. Experimental results show that this method outperforms other methods in terms of classification accuracy and speed.
APPLIED SCIENCES-BASEL
(2023)
Article
Optics
Yu Cheng, Xin-Yu Zhao, Li-Jing Li, Ming-Jie Sun
Summary: This study proposes an improved first-photon imaging scheme that independently reconstructs depth by optimizing the denoising method, resulting in improved quality of depth reconstruction. Experimental results demonstrate that the proposed scheme adapts to different noise environments and achieves better results, especially when the SBR is 1.0.
Article
Computer Science, Information Systems
Mingzheng Hou, Ziliang Feng, Haobo Wang, Zhiwei Shen, Sheng Li
Summary: This paper proposes a novel single-image super-resolution method that integrates image clustering, sparse representation, and linear regression to improve image quality. Experimental results show that the algorithm outperforms other competitive methods in terms of both qualitative and quantitative aspects, with noticeably faster computational speed.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chang Wang, Yang Wu, Yi Yu, Jun Qiang Zhao
Summary: In this study, an improved multi-modality image fusion method was proposed by combining the joint patch clustering-based adaptive dictionary and sparse representation to address the issue of gray inconsistency caused by the maximum L-1 norm fusion rule. Through quantitative evaluation and comparative experiments, it was demonstrated that the method has superiority in fusion metrics, image quality, and edge preservation.
MACHINE VISION AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Yue Pan, Tianye Lan, Chongyang Xu, Chengfang Zhang, Ziliang Feng
Summary: This paper reviews the recent advances in pixel-level image fusion based on convolutional sparse representation (CSR) and discusses the future trends of CSR-based image fusion.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Arash Abdi, Mohammad Rahmati, Mohammad M. Ebadzadeh
Summary: In this paper, a new discriminative dictionary learning algorithm is proposed, which embeds an entropy-based criterion in the objective function to enforce a proper structure for dictionary items. Experimental results demonstrate that the algorithm outperforms other methods on various real-world image datasets.
PATTERN RECOGNITION
(2021)
Article
Engineering, Biomedical
Feng-Ping An, Xing-min Ma, Lei Bai
Summary: This paper proposed an end-to-end unsupervised deep learning model for image fusion, which addresses the issues of supervised learning, image fusion weight map setting and noise. An optimized sparse representation method was also introduced to further improve the quality of the fusion results. The experiments demonstrated that the proposed method outperformed mainstream machine learning and deep learning image fusion methods in terms of quality evaluation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)
