基于深度迁移学习的天气图像识别

当对天气图像等场景复杂和特征不明显的图像进行识别时，往往存在识别率不高和特征冗余等问题。基于此，本文提出了一种基于深度迁移学习的图像分类算法。该算法利用ImageNet数据集的模型参数构建ResNeXt、Xception以及SENet 3种网络模型提取图像特征，采用领域自适应的判别联合分布自适应算法来相似化特征向量，完成高质量的特征表示，并以其结果为准则融合模型特征，将融合特征经过多层感知机训练以实现高准确率识别的图像分类。实验结果表明，该算法的性能优于传统的单一网络模型，进一步提升了图像分类准确率的上限。

Weather image recognition based on fusing deep transfer learning

When recognizing images with complex scenes and obscure features such as weather images, there are often problems such as low recognition rate and feature redundancy. Based on this, an image classification algorithm based on deep transfer learning is proposed in this paper. The algorithm uses the model parameters of ImageNet dataset to construct three network models, ResNeXt, Xception and SENet, to extract image features, and uses a domain-adaptive discriminative joint distribution adaptive algorithm to resemble the feature vectors to complete a high-quality feature representation, and uses the result as a criterion to fuse the model features, and trains the fused features through a multilayer perceptron to achieve image classification with high accuracy recognition. The experimental results show that the algorithm outperforms the traditional single network model and further improves the upper limit of image classification accuracy.