Unsupervised change detection based on supervised or semisupervised classifiers has achieved strong adaptability and robustness to obtain satisfactory change detection results. However, these methods suffer from an issue that it is hard to collect reliable training samples in an unsupervised manner. In this article, a group self-paced learning (GSPL) framework is proposed to mine the reliable training samples. In the proposed method, each sample is assigned a weight to indicate its reliability. The proposed scheme is able to learn the weighted samples and update the weights iteratively in a self-paced manner to identify the reliable training samples. In the phase of updating weights, a grouping strategy is designed to avoid selecting training samples from homogeneous regions. Furthermore, a novel time-varying self-paced regularizer is proposed to automatically determine the learning scheme of self-paced learning. Finally, three classifiers, including SoftMax, backpropagation neural network, and support vector machine, are investigated under this proposed framework. Experiments on five change detection data sets demonstrate that the proposed framework can significantly outperform those state-of-art methods for change detection in terms of accuracy and robustness.

中文翻译：

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使用时变正则化器进行无监督变化检测的组自定进度学习

基于有监督或半监督分类器的无监督变化检测具有很强的适应性和鲁棒性，可以获得令人满意的变化检测结果。然而，这些方法存在一个问题，即很难以无监督的方式收集可靠的训练样本。在本文中，提出了一种群体自定进度学习（GSPL）框架来挖掘可靠的训练样本。在所提出的方法中，每个样本都被分配一个权重来表示其可靠性。所提出的方案能够学习加权样本并以自定进度的方式迭代更新权重以识别可靠的训练样本。在更新权重阶段，设计了一种分组策略，以避免从同质区域中选择训练样本。此外，提出了一种新颖的时变自定进度正则化器来自动确定自定进度学习的学习方案。最后，在该框架下研究了三种分类器，包括 SoftMax、反向传播神经网络和支持向量机。在五个变化检测数据集上的实验表明，所提出的框架在准确性和鲁棒性方面可以显着优于那些最先进的变化检测方法。