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Automatic Weight Allocation: optimizing remote sensing image retrieval from contrastive learning perspective

Multimedia Systems 2025年第3期31卷 1-19页

作者： Wang, Sijia Ge, Yun Liu, Qiyang Zeng, Yan School of Software Nanchang Hangkong University Nanchang China Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang China

Traditional supervised learning methods achieve remarkable performance in high-resolution remote sensing image retrieval, but are limited by the dependence on large-scale annotated images. Contrastive learning can leverage unlabeled images to learn powerful visual features, demonstrating its potential in many unsupervised tasks. Moreover, hash algorithms show significant potential in the field of image retrieval with their advantages in efficiency and storage. Therefore, we propose the Contrastive Hashing Framework based on Automatic Weight Allocation. The framework employs a two-stage training strategy. In the feature learning stage, we propose the Automatic Weighted Contrastive Loss (AWCLoss). It incorporates Gaussian weighting and dynamic adjustment strategies to improve loss functions, enabling them to focus on the distinctiveness and importance of samples. Gaussian weighting assigns different weight values based on the similarity of sample pairs, enhancing the learning of critical sample pairs. Meanwhile, the dynamic adjustment strategy sets a threshold to identify hard negative samples and then adjusts the weight values to weaken the model from being disturbed by hard negative samples. In the hashing learning stage, a hashing layer is added to the end of the network, which converts high-dimensional representations into hash codes. The Quantization loss is introduced to learn the hash codes so that the semantic similarity structure between data can be preserved in hamming space. Additionally, the AWCLoss is utilized to enhance the discriminative power of the hash codes. Extensive experiments on three remotely sensed datasets UCM, AID and NWPU-RESISC45 have demonstrated the significant superiority of our approach in remote sensing image retrieval. Our source code is available at https://***/WANGSJ77/AWCH .

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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Reflecting topology consistency and abnormality via learnable attentions for airway labeling

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International journal of computer assisted radiology and surgery 2025年 2025 May 6页

作者： Chenyu Li Minghui Zhang Chuyan Zhang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai People's Republic of China. Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai People's Republic of China. Shanghai Key Laboratory of Flexible Medical Robotics Tongren Hospital Shanghai Jiao Tong University Shanghai People's Republic of China. Institute of Medical Robotics Shanghai Jiao Tong University Shanghai People's Republic of China. yungu@***. Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai People's Republic of China. yungu@***. Shanghai Key Laboratory of Flexible Medical Robotics Tongren Hospital Shanghai Jiao Tong University Shanghai People's Republic of China. yungu@***.

PURPOSE:Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway labeling is challenging due to significant anatomical variations. Previous methods are prone to generate inconsistent predictions, hindering preoperative planning and intraoperative navigation. This paper aims to enhance topological consistency and improve the detection of abnormal airway branches. METHODS:We propose a transformer-based framework incorporating two modules: the soft subtree consistency (SSC) and the abnormal branch saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous features aggregation between normal and abnormal nodes. RESULTS:Evaluated on a challenging dataset characterized by severe airway deformities, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains an 83.7% subsegmental accuracy, along with a 3.1% increase in segmental subtree consistency, a 45.2% increase in abnormal branch recall. Notably, the method demonstrates robust performance in cases with airway deformities, ensuring consistent and accurate labeling. CONCLUSION:The enhanced topological consistency and robust identification of abnormal branches provided by our method offer an accurate and robust solution for airway labeling, with potential to improve the precision and safety of bronchoscopy procedures.

关键词： Airway anatomical labeling Anomaly detection Structural prior Transformer

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Contour detection network simulating the primary visual pathway

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Digital Signal processing 2025年 165卷

作者： Ke Chen Yingle Fan Tao Fang Laboratory of Pattern Recognition and Image Processing Hangzhou Dianzi University Hangzhou 310018 Zhejiang China

Biological vision exhibits exceptional contour perception capabilities. In view of this, research on contour detection guided by biological vision is gradually gaining attention. Inspired by the transmission and processing of visual signals in the primary visual pathway, this study proposes a lightweight contour detection network called the Primary-Visual-Pathway UNet (PVP-UNet), comprising an encoder and a decoder. Primarily, inspiration from the binocular vision mechanism, we transmit the original image through dual deformable convolution modules that simulate the receptive fields of the left and right retinas in the encoder. Utilizing the characteristic of the optic chiasm, the output features of the retinal layers are split, swapped, and merged for further processing. Subsequently, dilated convolution modules and normal convolution modules are involved to simulate the magnocellular (M) and parvocellular (P) pathways of the lateral geniculate nucleus (LGN), respectively. An inhibition module was designed based on the suppression mechanism of classical/non-classical receptive fields (CRF/NCRF) in the primary visual cortex (V1). Additionally, the interconnection pattern of the inhibition modules is deployed by leveraging the aggregation characteristics of simple cells to complex cells in the V1 layer. Inspired by the feedback mechanism of visual information, the feature fusion module is introduced in the decoder to integrate features from different encoding layers in the reverse direction of signal transmission along the primary visual pathway. Our experiments show that the respective Optimal Dataset Scale (ODS) scores achieve 0.811, 0.756, and 0.896 on BSDS500, NYUD, and BIPED datasets. The experimental results demonstrate that the proposed network effectively suppresses background noise and highlights primary contours, exhibiting excellent detection performance on the tested datasets. The code is available at https://***/k3chencoco/PVP-UNet .

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A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

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Chinese Journal of Electronics 2019年第4期28卷 764-772页

作者： CHEN Guoyu LI Junhua Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University

The development of algorithms to solve Many-objective optimization problems(MaOPs) has attracted significant research interest in recent *** various types of Pareto front(PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm(RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector *** concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

关键词： Many-objective optimization Reference vector Research mode Evolutionary algorithm

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A novel probabilistic data association algorithm based on multiple model particle filter

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Journal of Information and Computational Science 2011年第13期8卷 2759-2766页

作者： Hu, Zhentao Liu, Xianxing Jin, Yong Hou, Yandong Laboratory of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

Aiming at the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutters environment, a novel probabilistic data association algorithm based on multiple model particle filter is proposed in this paper. Firstly, the evolution of system state model is introduced in the sampling process of particle, and the joint sampling is completed. Secondly, one-step prediction of measurement and the covariance matrix of residual is constructed by the calculating the mean and variance of particle set with model information. Finally, the confirmation principle of echo from the probabilistic data association algorithm is used to realize the reasonable utilization. The theoretical analysis and experimental results show the filter precision is significantly superior to IMMUKF-PDA and close to IMMPF-PDA, and the computational complexity is almost one-half of IMMPF-PDA under the same condition. 1548-7741/Copyright © 2011 Binary Information Press.

关键词： Covariance matrix

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Determining pose of 3D objects with curved surfaces

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1996年第1期18卷 52-57页

作者： Chen, JL Stockman, GC Pattern Recognition and Image Processing Laboratory Michigan State University East Lansing MI USA

A method is presented for computing the pose of rigid 3D objects with arbitrary curved surfaces. Given an input image and a candidate object model and aspect, the method will verify whether or not the object is present acid if so, report pose parameters. The curvature method of Basri and Ullman is used to model points on the object rim, while stereo matching is used for internal edge points. The model allows an object edgemap to be predicted from pose parameters. Pose is computed via an iterative search for the best pose parameters. Heuristics are used so that matching can succeed in the presence of occlusion and artifact and without resorting to use of corresponding salient feature points. Bench tests and simulations show that the method almost always converges to ground truth pose parameters for a variety of objects and for a broad set of starting parameters in the same aspect.

关键词： pose determination 3D objects object tracking object modeling image matching recognition by alignment

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Deep Learning in Palmprint recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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Combining local binary patterns for scene recognition

Journal of Software

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Journal of Software 2014年第1期9卷 203-210页

作者： Song, Minguang Guo, Ping Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Difference Binary pattern (LDBP) also was proposed and performed better than that of PACT. LDBP is based on the comparisons between center pixel and its neighboring pixels, but the relationship among neighbor pixels is not considered. In this paper, we propose to combine Local Neighbor Binary pattern (LNBP) with LDBP to construct a spatial representation for scene recognition, because that LNBP can provide complementary information regarding neighboring pixels for LDBP. Experiments on widely used datasets demonstrate that the performance of image recognition is further improved with proposed method. © 2014 ACADEMY PUBLISHER.

关键词： Pixels

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

Patterns

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patterns 2025年

作者： Yi-Fan Li Xiaoyong Pan Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs.

关键词： nuclear localization signals prediction protein language model database mining deep learning interpretation

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