Pattern Analysis and Applications最新文献

{"title":"Design of convolutional neural network-based layer operator for image denoising using fourth-order PDE","authors":"Mahima Lakra","doi":"10.1007/s10044-024-01313-9","DOIUrl":"https://doi.org/10.1007/s10044-024-01313-9","url":null,"abstract":"","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lsf-rdd: a local sensing feature network for road damage detection","authors":"Qihan He, Zhongxu Li, Wenyuan Yang","doi":"10.1007/s10044-024-01314-8","DOIUrl":"https://doi.org/10.1007/s10044-024-01314-8","url":null,"abstract":"","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of dynamic bi-clustering based on improved genetic algorithm for microarray data","authors":"Pintu Kumar Ram, Pratyay Kuila","doi":"10.1007/s10044-024-01309-5","DOIUrl":"https://doi.org/10.1007/s10044-024-01309-5","url":null,"abstract":"<p>Due to the nature of microarray data, the analysis of genes/features for disease diagnosis is a challenging task. Generally, the data comes in the form of a 2D matrix, where the row represents the genes and the column indicates the various conditions. Bi-clustering is an emerging technique that can efficiently reveal patterns of genes. It can perform simultaneously with a subset of genes and conditions. Inspired by this, dynamic bi-clustering based on an improved genetic algorithm (GA) is proposed. The chromosomes are efficiently designed. In addition, the fitness function is derived by considering multiple conflicting objectives to measure the quality of a cluster. A novel mutation is designed by the correlation technique. The crossover and mutation rates are dynamically changed. The obtained outcomes of the proposed approach are compared with the various existing approaches, such as traditional GA, the dynamic dame parallel GA, the evolutionary local search algorithm, bi-phase evolutionary searching, and the evolutionary bi-clustering algorithm. Further, statistical tests such as the analysis of variance and Friedman test are executed to show the significance of the proposed model. A biological analysis is also performed.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"K-homogeneous nearest neighbor-driven discriminant graph coupled nonnegative matrix factorization for low-resolution image recognition","authors":"Jihong Pei, Yebin Chen, Yang Zhao, Xuan Yang","doi":"10.1007/s10044-024-01316-6","DOIUrl":"https://doi.org/10.1007/s10044-024-01316-6","url":null,"abstract":"<p>The coupled nonnegative matrix factorization method can utilize the information in high-resolution images to assist in the extraction of local semantic features of low-resolution (LR) images. However, since the supervised information is not fully considered in the existing methods, the discriminative performance of the extracted features is limited. In this paper, <i>k</i>-homogeneous nearest neighbor-driven discriminant graph coupled nonnegative matrix factorization (KHNNDG-CNMF) is proposed for low-resolution image recognition (LRIR). In the proposed approach, a <i>k</i>-homogeneous nearest neighbor-driven discriminant graph (KHNNDG) is constructed, which is a discriminant graph matrix constructed within the geometrical nearest neighbors of <i>k</i> homogeneous samples. In discriminant graph construction methods, the two problems of insufficient utilization of data information in local neighborhoods existing in the previous neighbor graph and the inability to reflect the real data distribution in the local neighborhood can be improved. According to the geometrical spatial distribution of samples, the KHNNDG can adaptively reflect the relationship between intra-class samples and inter-class samples, and relatively few hyperparameters are required. Therefore, the coupled nonnegative matrix factorization algorithm combined with the KHNNDG embedding regularized term can more accurately and effectively utilize the local discriminativeness of the original space to constrain the coupled features. Furthermore, we further strengthen the class separability among features by incorporating a consistent projection constraint from features to labels. The proposed algorithm performs experiments on 6 image databases. Three different LR images are involved in the experiment. Compared to the best-performing comparative method, the proposed method shows an average improvement of approximately 2.36% in recognition performance. Particularly in tasks involving extremely LR levels (<span>(8times 7)</span>), the proposed method achieves an average improvement of 3.03%. Ablation experiments show that each module in the method can improve the recognition performance of LR images.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-based supervised contrastive learning on fine-grained image classification","authors":"Qian Li, Weining Wu","doi":"10.1007/s10044-024-01317-5","DOIUrl":"https://doi.org/10.1007/s10044-024-01317-5","url":null,"abstract":"<p>To solve the problem of fine-grained image classification performance caused by intra-class diversity and inter-class similarity in fine-grained images, we propose an Attention-based Supervised Contrastive (ASC) algorithm for fine-grained image classification. The method involves three stages: firstly, local parts are generated by a multi-attention module for constructing contrastive objectives to filter useless background information; an attention-based supervised contrastive framework is introduced to pre-train an encoder network and learn generalized features by pulling positive pairs closer while pushing negatives apart. Finally, we use cross-entropy to fine-tune the model pre-trained in the second stage to obtain classification results. Comprehensive experiments on CUB-200-2011, FGVC-Aircraft, and Stanford Cars datasets demonstrate the effectiveness of the proposed method.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight weld defect recognition algorithm based on convolutional neural networks","authors":"Wenjie Zhao, Dan Li, Feihu Xu","doi":"10.1007/s10044-024-01315-7","DOIUrl":"https://doi.org/10.1007/s10044-024-01315-7","url":null,"abstract":"<p>This paper proposes a lightweight weld defect-recognition algorithm based on a convolutional neural network that is appropriate for weld defect recognition in industrial welding. Specifically, the developed scheme relies on the original SqueezeNet model. However, we improve the fire module to reduce the model’s parameter cardinality, introduce the ECA module to strengthen the learning of feature channels and improve the feature extraction ability of the overall model. The experimental results highlight that our algorithm’s average recognition rate on the overall defects of welding depressions, welding holes, and welding burrs reaches 97.50%. Note that although our model requires substantially fewer parameters, its recognition effect is significantly improved. Our algorithm’s feasibility is verified on the test data and challenged against current weld defect identification algorithms, demonstrating its enhanced identification role and application prospect.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMRU-Net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions","authors":"Shangwang Liu, Peixia Wang, Yinghai Lin, Bingyan Zhou","doi":"10.1007/s10044-024-01307-7","DOIUrl":"https://doi.org/10.1007/s10044-024-01307-7","url":null,"abstract":"<p>Skin disease image segmentation faces two major challenges: the complex and varied lesion morphology and the presence of interfering image backgrounds. To address these difficulties in skin disease image segmentation, we propose a Residual U-Net architecture with Channel-Space Separate Attention based on depthwise separable convolutions. The multi-scale residual U-Net modules in the encoder efficiently capture multi-scale texture information in lesions and backgrounds within a single stage, overcoming the limitations of U-Net in extracting just local features. The introduction of ConvMixer Block for global contextual modeling contributes to suppress complex background interference and enhances the overall understanding of lesion morphology. Additionally, we employ a Channel-Space Separate Attention mechanism with depthwise separable convolutions(CSSA-DSC) for feature fusion, effectively addressing the limited expressiveness issue associated with U-Net’s direct skip-connection concatenation. Experimental results on the PH2, ISIC 2017, and ISIC 2018 datasets demonstrate our method’s strong multi-scale modeling and feature expression capabilities.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general Seeds-Counting pipeline using deep-learning model","authors":"Zeonlung Pun, Xinyu Tian, Shan Gao","doi":"10.1007/s10044-024-01304-w","DOIUrl":"https://doi.org/10.1007/s10044-024-01304-w","url":null,"abstract":"<p>This study presents a novel Seeds-Counting pipeline harnessing deep learning algorithms to facilitate the automation of yield prediction prior to harvesting, a crucial component of the breeding process. Unlike existing methods that often cater to a single seed species or those with similar shapes, our approach is capable of accurately estimating the number of seeds across a diverse range of species. The pipeline incorporates a classification network for seed image categorization, along with object detection models specifically tailored to accommodate the morphologies of different seeds. By integrating a seed classifier, three distinct seed detectors, and post-processing filters, our method not only showcases exceptional accuracy but also exhibits robust generalization capabilities across various conditions. Demonstrating an error rate of less than 2% in the test set and achieving accuracy rates exceeding 97% in the extended set, the proposed pipeline offers a viable and efficient solution for high-throughput phenotyping and precision agriculture, effectively overcoming the challenges posed by the diverse morphologies of seeds.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SRU-Net: a novel spatiotemporal attention network for sclera segmentation and recognition","authors":"Tara Mashayekhbakhsh, Saeed Meshgini, Tohid Yousefi Rezaii, Somayeh Makouei","doi":"10.1007/s10044-024-01301-z","DOIUrl":"https://doi.org/10.1007/s10044-024-01301-z","url":null,"abstract":"<p>Segmenting sclera images for effective recognition under non-cooperative conditions poses a significant challenge due to the prevalent noise. While U-Net-based methods have shown success, their limitations in accurately segmenting objects with varying shapes necessitate innovative approaches. This paper introduces the spatiotemporal residual encoding and decoding network (SRU-Net), featuring multi-spatiotemporal feature integration (Ms-FI) modules and attention-pool mechanisms to enhance segmentation accuracy and robustness. Ms-FI modules within SRU-Net’s encoders and decoders identify salient feature regions and prune responses, while attention-pool modules improve segmentation robustness. To assess the proposed SRU-Net, we conducted experiments using six datasets, employing precision, recall, and F1-score metrics. The experimental results demonstrate the superiority of SRU-Net over state-of-the-art methods. Specifically, SRU-Net achieves F1-score values of 94.58%, 98.31%, 98.49%, 97.52%, 95.3%, 97.47%, and 93.11% for MSD, MASD, SVBPI, MASD+MSD, UBIRIS.v1, UBIRIS.v2, and MICHE, respectively. Further evaluation in recognition tasks, with metrics such as AUC, EER, VER@0.1%FAR, and VER@1%FAR considered for the six datasets. The proposed pipeline, comprising SRU-Net and auto encoders (AE), outperforms previous research for all datasets. Particularly noteworthy is the comparison of EER, where SRU-Net + AE exhibits the best recognition results, achieving an EER of 9.42%, 3.81%, and 5.73% for MSD, MASD, and MICHE datasets, respectively.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal generative explainers using counterfactual inference: a case study on the Morpho-MNIST dataset","authors":"Will Taylor-Melanson, Zahra Sadeghi, Stan Matwin","doi":"10.1007/s10044-024-01306-8","DOIUrl":"https://doi.org/10.1007/s10044-024-01306-8","url":null,"abstract":"<p>In this paper, we propose leveraging causal generative learning as an interpretable tool for explaining image classifiers. Specifically, we present a generative counterfactual inference approach to study the influence of visual features (pixels) as well as causal factors through generative learning. To this end, we first uncover the most influential pixels on a classifier’s decision by computing both Shapely and contrastive explanations for counterfactual images with different attribute values. We then establish a Monte Carlo mechanism using the generator of a causal generative model in order to adapt Shapley explainers to produce feature importances for the human-interpretable attributes of a causal dataset. This method is applied to the case where a classifier has been trained exclusively on the images of the causal dataset. Finally, we present optimization methods for creating counterfactual explanations of classifiers by means of counterfactual inference, proposing straightforward approaches for both differentiable and arbitrary classifiers. We exploit the Morpho-MNIST causal dataset as a case study for exploring our proposed methods for generating counterfactual explanations. However, our methods are applicable also to other causal datasets containing image data. We employ visual explanation methods from the OmnixAI open source toolkit to compare them with our proposed methods. By employing quantitative metrics to measure the interpretability of counterfactual explanations, we find that our proposed methods of counterfactual explanation offer more interpretable explanations compared to those generated from OmnixAI. This finding suggests that our methods are well-suited for generating highly interpretable counterfactual explanations on causal datasets.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}