- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[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) - Semi-supervised convolutional neural networks with label propagation for image classification
摘要： Over the past several years, deep learning has achieved promising performance in many visual tasks, e.g., face verification and object classification. However, a limited number of labeled training samples existing in practical applications is still a huge bottleneck for achieving a satisfactory performance. In this paper, we integrate class estimation of unlabeled training data with deep learning model which generates a novel semi-supervised convolutional neural network (SSCNN) trained by both the labeled training data and unlabeled data. In the framework of SSCNN, the deep convolution feature extraction and the class estimation of the unlabeled data are jointly learned. Specifically, deep convolution features are learned from the labeled training data and unlabeled data with confident class estimation. After the deep features are obtained, the label propagation algorithm is utilized to estimate the identities of unlabeled training samples. The alternative optimization of SSCNN makes the class estimation of unlabeled data more and more accurate due to the learned CNN feature more and more discriminative. We compared the proposed SSCNN with some representative semi-supervised learning approaches on MINIST and Cifar-10 databases. Extensive experiments on landmark databases show the effectiveness of our semi-supervised deep learning framework.
关键词： convolutional neural network,semi-supervised learning,label propagation
更新于2025-09-23 15:21:01
-
[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 - A Novel Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification
摘要： For hyperspectral image classification, we present a novel graph based semi-supervised classification method that learns from similarity and dissimilarity on labeled and unlabeled data, which contain both the adjacency graph and the dissimilar graph. Since manifold learning approach is capable of exploring the manifold geometry of data, it is suitable for calculating the adjacency graph with label similarity. A manifold learning method was utilized to calculate the adjacency graph. Dissimilarity among examples probably be used to construct the dissimilar graph, which is hard to grasp. The dissimilar probability was proposed to construct the dissimilar graph, which has effectively improved the classification accuracy of hyperspectral data in experiment.
关键词： Graph,Semi-Supervised Classification,Hyperspectral Remote Sensing,Label Propagation
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Building Detection and Segmentation Using a CNN with Automatically Generated Training Data
摘要： Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.
关键词： Label propagation,Image segmentation,Automatic image annotation,Deep learning
更新于2025-09-09 09:28:46