- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning
摘要： The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.
关键词： remote sensing (RS) images,Semi-supervised classification,ensemble learning (EL),scene classification,Convolutional neural networks (CNNs)
更新于2025-09-23 15:23:52
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Multi-scale sifting for mammographic mass detection and segmentation
摘要： Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance. In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture. It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance. The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns. This method can accurately segment masses of various shapes and sizes from the background tissue. To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost). The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique. This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP. On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost. On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost. The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.
关键词： Morphological sifting,Mammography,Breast mass detection and segmentation,Cascaded random forest,Ensemble learning
更新于2025-09-23 15:22:29
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Retraining: A Simple Way to Improve the Ensemble Accuracy of Deep Neural Networks for Image Classification
摘要： In this paper, we propose a new heuristic training procedure to help a deep neural network (DNN) repeatedly escape from a local minimum and move to a better local minimum. Our method repeats the following processes multiple times: randomly reinitializing the weights of the last layer of a converged DNN while preserving the weights of the remaining layers, and then conducting a new round of training. The motivation is to make the training in the new round learn better parameters based on the “good” initial parameters learned in the previous round. With multiple randomly initialized DNNs trained based on our training procedure, we can obtain an ensemble of DNNs that are more accurate and diverse compared with the normal training procedure. We call this framework “retraining”. Experiments on eight DNN models show that our method generally outperforms the state-of-the-art ensemble learning methods. We also provide two variants of the retraining framework to tackle the tasks of ensemble learning in which 1) DNNs exhibit very high training accuracies (e.g., >95%) and 2) DNNs are too computationally expensive to train.
关键词： ensemble learning,retraining,local minima,deep neural networks,image classification
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Identifying and Correcting Mislabeled Satellite Image Data by Iterative Ordering of Ensemble Margins
摘要： The accuracy of a supervised classifier is directly influenced by the quality of the training data used. However, real-world data often suffers from mislabelling issues. To handle the mislabeling problem, we propose an ensemble margin-based mislabeled training data identification, elimination and correction approach based on data ordering. A powerful ensemble method, random forest, is at the core of our algorithms design. The effectiveness of our methods is demonstrated in performing mapping of land covers. A comparative analysis is conducted with respect to the majority vote filter, a popular ensemble-based mislabeled data filter.
关键词： mislabelling,random forest,Ensemble learning,margin,training data,remote sensing
更新于2025-09-12 10:27:22
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Semi-Supervised Scene Classification for Remote Sensing Images Based on CNN and Ensemble Learning
摘要： The special characteristic of remote sensing (RS) images being large scale while only low number of labeled samples available in practical applications has been obstacle to the development of RS image classification. In this paper, a novel semi-supervised framework is proposed. The high-capacity convolutional neural networks (CNN) are adopted to extract preliminary image features. The strategy of ensemble learning is then utilized to establish discriminative image representations by exploring intrinsic information of available data. Plain supervised learning is finally performed to obtain classification results. To verify the efficacy of our work, we compare it with mainstream feature representation and semi-supervised approaches. Experimental results show that by utilizing CNN features and ensemble learning, our framework can obtain more effective image representations and achieve superior results compared with other paradigms of semi-supervised classification.
关键词： convolutional neural network,ensemble learning,remote sensing images,Semi-supervised classification,scene classification
更新于2025-09-11 14:15:04
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Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
摘要： The adoption of large-scale iris recognition systems around the world has brought the importance of detecting presentation attack images (textured contact lenses and printouts). This work presents a new approach in iris Presentation Attack Detection (PAD), by exploring combinations of Convolutional Neural Networks (CNNs) and transformed input spaces through binarized statistical image features (BSIF). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand. An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris 2017 competition both for intra- and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.
关键词： Convolutional Neural Networks (CNNs),Ensemble Learning,Binarized Statistical Image Features (BSIF),Presentation Attack Detection (PAD),Iris recognition
更新于2025-09-11 14:15:04
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[IEEE 2018 IEEE International Conference on Mechatronics and Automation (ICMA) - Changchun (2018.8.5-2018.8.8)] 2018 IEEE International Conference on Mechatronics and Automation (ICMA) - Weld Seam Type Recognition System Based on Structured Light Vision and Ensemble Learning
摘要： In this paper, we propose a weld seam recognition system based on structured light vision and ensemble learning. The proposed system consists of an industrial robot, a structured light vision sensor and a computer. The recognition procedures of proposed system include weld seam feature extraction and weld seam classification. In feature extraction part, the input images are processed by the following steps: noise filtration, laser stripe pattern extraction, main line extraction, edge points detection and feature computation. In classification part, ensemble learning models including BP-Adaboost and KNN-Adaboost are established to classify the images by the feature extracted. The experiment results validate the effectiveness and robustness of the proposed recognition system.
关键词： ensemble learning algorithm,structured light vision,feature extraction,weld seam type recognition
更新于2025-09-09 09:28:46
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11258 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part III) || Face Recognition Based on Multi-view
摘要： Face recognition is an important research area in human-computer. To solve the problem about the inaccuracy and incompleteness of feature extraction and recognition, an ensemble learning method on face recognition is proposed in this paper. This method is a combination of a variety of feature extraction and classi?cation ensemble technology. In feature extraction, wavelet transform and edge detection are used for extracting features. In classi?cation recognition, the K nearest neighbor (KNN) classi?er, wavelet neural network (WNN) and support vector machine (SVM) are used for preliminary identi?cation. Each classi?er corresponds to a feature method and then the classi?cation of the three views are constructed. The ?nal output results are integrated by voting strategy. Experimental results show that this method can improve the identi?cation rate compared with the single classi?er.
关键词： Feature extraction,Multi-view,Ensemble learning,Face recognition,Voting
更新于2025-09-04 15:30:14