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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031784552

The proceedings contain 958 papers. The special focus in this conference is on pattern recognition. The topics include: Supervised Mixup: Protecting the Likely Classes for Adversarial Robustness;IFFusion: Illumination-Free Fusion Network for Infrared and Visible images;infrared and Visible image Fusion Method Based on Learnable Joint Sparse Low-Rank Decomposition;Glare-SNet: Unsupervised Glare Suppression Balance Network;Learning to Detect Lithography Defects in SEM images;time-Aware Intent Contrastive Learning with Rare-Class Sample Generator for Sequential Recommendation;UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning;effects of Primary Capsule Shapes and Sizes in Capsule Networks;ASwin-YOLO: Attention – Swin Transformers in YOLOv7 for Air-to-Air Unmanned Aerial Vehicle Detection;quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity;dualswin-Ynet: A Novel Bimodal Fusion Network for Ship Detection in remote sensing images;STMAE: Spatial Temporal Masked Auto-Encoder for Traffic Forecasting;BF-UNet: Bi-level Routing Attention U-shaped Network Based on Explicit Visual Prompt;learning Dynamic Representations in Large Language Models for Evolving Data Streams;attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection;pneumonia Classification in Chest X-Ray images Using Explainable Slot-Attention Mechanism;SegNet-ATT: Cross-Channel and Spatial Attention-Enhanced U-Net for Semantic Segmentation of Flood Affected Areas;WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking;detection of Oral Potentially Malignant Lesions Through Transformer-Based Segmentation Models;ROI-Aware Dynamic Network Quantization for Neural Video Compression;secureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning;TVT: Training-Free Vision Transformer Search on Tiny Datasets;one-Shot Classification Is Enough for Automatic Label Mapping;sustainable and

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031784460

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031783111

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031781940

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031781889

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031781858

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Adaptive remote sensing image Attribute Learning for Active Object Detection 25

Adaptive Remote Sensing Image Attribute Learning for Active ...

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25th International Conference on pattern recognition (ICPR)

作者： Xu, Nuo Huo, Chunlei Guo, Jiacheng Liu, Yiwei Wang, Jian Pan, Chunhong Chinese Acad Sci Univ Chinese Acad Sci Sch Artificial Intelligence NLPRInst Automat Beijing Peoples R China Beijing Informat Sci & Technol Univ Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Peoples R China Beijing Union Univ Coll Robot Beijing Peoples R China

ISBN: (纸本)9781728188089

In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging configuration and detection performance, and do not take into account the importance of detection performance feedback for improving image quality. Therefore, detection performance is limited by the passive nature of the conventional object detection framework. In order to solve the above limitations, this paper takes adaptive brightness adjustment and scale adjustment as examples, and proposes an active object detection method based on deep reinforcement learning. The goal of adaptive image attribute learning is to maximize the detection performance. With the help of active object detection and image attribute adjustment strategies, low-quality images can be converted into high-quality images, and the overall performance is improved without retraining the detector.

关键词： Object detection

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27th International Conference on pattern recognition, ICPR 2024

27th International Conference on Pattern Recognition, ICPR 2...

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031783821

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27th International Conference on pattern recognition, ICPR 2024

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27th International Conference on pattern recognition, ICPR 2024

ISBN: (纸本)9783031781278

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Mahalanobis distance based accuracy prediction models for Sentinel-2 image Scene Classification

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INTERNATIONAL JOURNAL OF remote sensing 2022年第15-16期43卷 6001-6026页

作者： Raiyani, Kashyap Goncalves, Teresa Rato, Luis Barao, Miguel Univ Evora Sch Sci & Technol Dept Informat P-7000671 Evora Portugal

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become a prime component of any problem-solving. Examples include building classifiers for applications such as image/speech recognition, traffic prediction, product recommendation, virtual personal assistant (VPA), online fraud detection and many more. The performance of these developed classifiers is highly dependent upon the training dataset, and subsequently, without human intervention or true labels, the evaluation over unseen observations remains unknown. Using a statistical distance researchers did try to assess the model's goodness-of-fit and compared multiple independent models. Nonetheless, given a train-test split and different classifiers built over the training set, the question 'is it possible to find a prediction error using the relation between training and test set?' remains unsolved. In this article, we propose a generalized statistical distance-based method measuring the prediction uncertainty at a new query point. To be specific, we propose a Mahalanobis distance-based Evidence Function Model to measure the misclassification caused by K-Nearest Neighbours (KNN), Extra Trees (ET), and Convolutional Neural Network (CNN) models when classifying Sentinel-2 image into six scene classes (Water, Shadow, Cirrus, Cloud, Snow, Other). The performance of the proposed method was assessed over two different datasets: (i) the test set, with an overall mean prediction uncertainty detection of 62.99%, 29.80% and 31.51%, leading to a mean micro-F1 performance of 67.89%, 39.30%, and 38.29% for KNN, ET, and CNN, respectively;(ii) a water-body set, with prediction uncertainty detection of 22.27%, 42.08%, and 27.67%, leading to a micro-F1 performance of 34.70%, 58.96%, and 43.32%, respectively.

关键词： Supervised Learning Sentinel-2 image Scene Classification Mahalanobis Distance pattern recognition Classification Prediction Error

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