Pattern Recognition Letters最新文献

Achieving high performance on sketch-based image retrieval without real sketches for training 在没有真实草图训练的情况下，实现基于草图的图像检索的高性能

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-25 DOI: 10.1016/j.patrec.2025.04.018

Jose M. Saavedra , Christopher Stears , Waldo Campos

{"title":"Achieving high performance on sketch-based image retrieval without real sketches for training","authors":"Jose M. Saavedra , Christopher Stears , Waldo Campos","doi":"10.1016/j.patrec.2025.04.018","DOIUrl":"10.1016/j.patrec.2025.04.018","url":null,"abstract":"<div><div>Sketch-based image retrieval (SBIR) has become an attractive area in computer vision. Along with the advances in deep learning, we have seen more sophisticated models for SBIR that have shown increasingly better results. However, these models are still based on supervised learning strategies, requiring the availability of real sketch-photo pairs. Having a paired dataset is impractical in real environments (e.g. eCommerce), which can limit the massification of this technology. Therefore, based on advances in foundation models for extracting highly semantic features from images, we propose S3BIR-DINOv2, a self-supervised SBIR model using pseudo-sketches to address the absence of real sketches for training, learnable vectors to allow the model to hold only one encoder for processing the underlying two image modalities, contrastive learning and an adapted DINOv2 as the visual encoder. Our experiments show our model performs outstandingly in diverse public datasets without requiring real sketches for training. We reach an overall mAP of 61.10% in Flickr15K and 44.37% in the eCommerce dataset.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"193 ","pages":"Pages 94-100"},"PeriodicalIF":3.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CycleMatch: Cyclic pseudo-labeling distillation in semi-supervised medical image segmentation CycleMatch：半监督医学图像分割中的循环伪标记蒸馏

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-25 DOI: 10.1016/j.patrec.2025.04.014

Chunshi Wang , Chuan Xiong , Bin Zhao , Shuxue Ding

引用次数: 0

Cross-erasure enhanced network for occluded person re-identification 交叉擦除增强网络对闭塞者的再识别

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-25 DOI: 10.1016/j.patrec.2025.04.015

Yunzuo Zhang, Yuehui Yang, Weili Kang, Jiawen Zhen

引用次数: 0

Simulated annealing-based text clustering 模拟基于退火的文本聚类

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-25 DOI: 10.1016/j.patrec.2025.04.019

Nacim Fateh Chikhi

{"title":"Simulated annealing-based text clustering","authors":"Nacim Fateh Chikhi","doi":"10.1016/j.patrec.2025.04.019","DOIUrl":"10.1016/j.patrec.2025.04.019","url":null,"abstract":"<div><div>Like traditional K-means, the main drawback of spherical K-means is its high sensitivity to the initialization of centroids. This issue can cause the algorithm to converge to poor local optima, resulting in clusters that do not accurately reflect the true structure of the data. In this paper, we propose two new text clustering algorithms that are less sensitive to initialization and that significantly improve clustering performance. The first algorithm employs simulated annealing to avoid getting trapped in poor local optima. The second algorithm, a relaxed version of simulated annealing, also uses randomization to escape poor local optima but requires significantly fewer computations than the first algorithm. The two algorithms are extensively evaluated across more than thirty text datasets. Experimental results demonstrate that the proposed approaches significantly outperform well-established text clustering algorithms in terms of clustering quality. Furthermore, the second algorithm is as efficient as standard spherical K-means regarding clustering speed, as both have the same time complexity. Finally, an important advantage of the proposed algorithms is that they can be applied to other domains involving directional data, such as recommender systems, social network analysis, image analysis, and more.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"193 ","pages":"Pages 128-134"},"PeriodicalIF":3.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced generation of automatically labelled image segmentation datasets by advanced style interpreter deep architectures 基于高级解释器深度架构的自动标记图像分割数据集的增强生成

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-23 DOI: 10.1016/j.patrec.2025.04.021

Marcos Sergio Pacheco dos Santos Lima Junior , Ezequiel López-Rubio , Juan Miguel Ortiz-de-Lazcano-Lobato , José David Fernández-Rodríguez

引用次数: 0

SYNAuG: Exploiting synthetic data for data imbalance problems SYNAuG：利用合成数据解决数据不平衡问题

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-23 DOI: 10.1016/j.patrec.2025.04.013

Moon Ye-Bin , Nam Hyeon-Woo , Wonseok Choi , Nayeong Kim , Suha Kwak , Tae-Hyun Oh

引用次数: 0

Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks 通过多模态图神经网络进行电子显微镜图像和光谱采样的矿物分割

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-22 DOI: 10.1016/j.patrec.2025.04.012

Samuel Repka , Bořek Reich , Fedor Zolotarev , Tuomas Eerola , Pavel Zemčík

{"title":"Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks","authors":"Samuel Repka , Bořek Reich , Fedor Zolotarev , Tuomas Eerola , Pavel Zemčík","doi":"10.1016/j.patrec.2025.04.012","DOIUrl":"10.1016/j.patrec.2025.04.012","url":null,"abstract":"<div><div>We propose a novel Graph Neural Network-based method for segmentation based on data fusion of multimodal Scanning Electron Microscope (SEM) images. In most cases, Backscattered Electron (BSE) images obtained using SEM do not contain sufficient information for mineral segmentation. Therefore, imaging is often complemented with point-wise Energy-Dispersive X-ray Spectroscopy (EDS) spectral measurements that provide highly accurate information about the chemical composition but that are time-consuming to acquire. This motivates the use of sparse spectral data in conjunction with BSE images for mineral segmentation. The unstructured nature of the spectral data makes most traditional image fusion techniques unsuitable for BSE-EDS fusion. We propose using graph neural networks to fuse the two modalities and segment the mineral phases simultaneously. Our results demonstrate that providing EDS data for as few as 1% of BSE pixels produces accurate segmentation, enabling rapid analysis of mineral samples. The proposed data fusion pipeline is versatile and can be adapted to other domains that involve image data and point-wise measurements.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"193 ","pages":"Pages 79-85"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the representation of sparse stochastic matrices with state embedding 稀疏随机矩阵的状态嵌入表示

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-21 DOI: 10.1016/j.patrec.2025.04.011

Jugurta Montalvão, Gabriel Bastos, Rodrigo Sousa, Ataíde Gualberto

引用次数: 0

Remote blood pressure estimation using BVP signal features from facial videos 基于面部视频BVP信号特征的远程血压估计

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-21 DOI: 10.1016/j.patrec.2025.04.010

Xiujuan Zheng, Binghang Zou, Chang Zhang, Haiyan Tu

{"title":"Remote blood pressure estimation using BVP signal features from facial videos","authors":"Xiujuan Zheng, Binghang Zou, Chang Zhang, Haiyan Tu","doi":"10.1016/j.patrec.2025.04.010","DOIUrl":"10.1016/j.patrec.2025.04.010","url":null,"abstract":"<div><div>Pulse signals contain abundant cardiovascular functional information and can be used for blood pressure estimation. Remote photoplethysmography (rPPG) technology offers a solution to obtain pulse signals from facial videos and then to achieve continuous blood pressure estimation. However, rPPG is susceptible to external factors that lead to a decrease in pulse signal quality, which directly affects the accuracy and reliability of blood pressure estimation. Therefore, this paper proposes a method that integrates advanced signal processing techniques and pulse feature analysis to improve the accuracy of video-based blood pressure estimation. First, we used an adaptive chirp mode decomposition algorithm and a waveform quality analysis algorithm based on a correlation coefficient to suppress noise interference and ensure the effectiveness of the pulse features obtained. Then, we conducted pulse signal feature selection using the mean impact value algorithm and established a blood pressure estimation model based on a BP neural network. Finally, we updated the neural network BP using the sparrow search algorithm to obtain the optimal blood pressure estimation model. Through validation on a private dataset, the results show that the proposed method can meet the blood pressure measurement standards and effectively achieve remote blood pressure estimation.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"193 ","pages":"Pages 122-127"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET 多阶段中间融合多模式学习从CT和PET分类非小细胞肺癌亚型

IF 3.9 3区计算机科学

Pattern Recognition Letters Pub Date : 2025-04-21 DOI: 10.1016/j.patrec.2025.04.001

Fatih Aksu , Fabrizia Gelardi , Arturo Chiti , Paolo Soda

{"title":"Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET","authors":"Fatih Aksu , Fabrizia Gelardi , Arturo Chiti , Paolo Soda","doi":"10.1016/j.patrec.2025.04.001","DOIUrl":"10.1016/j.patrec.2025.04.001","url":null,"abstract":"<div><div>Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always feasible and may lead to clinical complications. This study presents MINT, a Multi-stage INTermediate fusion approach to classify NSCLC subtypes from CT and PET images. Our method integrates the two modalities at different stages of feature extraction, using voxel-wise fusion to exploit complementary information across varying abstraction levels while preserving spatial correlations. We compare our method against unimodal approaches using only CT or PET images to demonstrate the benefits of modality fusion, and further benchmark it against early and late fusion techniques to highlight the advantages of intermediate fusion during feature extraction. Additionally, we compare our model with the only existing intermediate fusion method for histological subtype classification using PET/CT images. Our results demonstrate that the proposed method outperforms all alternatives across key metrics, with an accuracy and AUC equal to 0.724 and 0.681, respectively. This non-invasive approach has the potential to significantly improve diagnostic accuracy, facilitate more informed treatment decisions, and advance personalized care in lung cancer management.</div></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":"193 ","pages":"Pages 86-93"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0