基于目标域局部近邻几何信息的域自适应图像分类方法

在许多实际工程应用中，训练场景（源域）和测试场景（目标域）的分布并不相同，如果将源域中训练的分类器直接应用到目标域，性能往往会出现大幅度下降。目前大多数域自适应方法以概率推导为基础。从图像特征表达的角度出发，针对自适应图像分类问题，提出一种新的基于协同特征的无监督方法。首先，所有源样本被作为字典；然后，距离目标样本最近的三个目标域样本被用来帮助鲁棒地表达局部近邻几何信息；最后，结合字典和局部近邻信息实现编码，并利用最近邻分类器完成分类。因为协同特征通过融合目标域局部近邻信息，获得了更强的鲁棒性和区分性，基于该特征编码的分类方法具有更好的分类性能。在域自适应数据集上的对比实验结果表明所提算法是有效的。

Domain adaptation image classification based on target local-neighbor geometrical information

In many real engineering applications, the distribution of training scenarios (source domain) and the distribution of testing scenarios (target domain) is different, thus the classification performance decreases sharply when simply applying the classifier trained in source domain directly to the target domain. At present, most of the existing domain adaptation methods are based on the probability-inference. For the problem of domain adaptation image classification, a collaborative representation based unsupervised method was proposed from the view of image representation. Firstly, all of the source samples were taken as the dictionary. Secondly, the three target samples closest to the target sample in the target domain were exploited to robustly represent the local-neighbor geometrical information. Thirdly, the target sample was encoded by combining the dictionary and the local-neighbor information. Finally, the classification was completed by using the nearest classifier. Since the collaborative representations have stronger robustness and discriminative ability by absorbing the target local-neighbor information, the classification method based on the new representations has better classification performance. The experimental results on the domain adaptation dataset confirm the effectiveness of the proposed method.