Two-stage natural scene image classification with noise discovering and label-correlation mining

In natural scene classification, it is common that one natural scene image may belong to multiple categories concurrently; as a result, multi-label learning has become a research hotspot. Despite recent rapid developments in multi-label learning, the increasing amount of high-dimensional data poses great challenges—such as redundant features and high computational costs—to conventional multi-label learning models. Most contemporary strategies for dealing with this issue depend on forcing feature learning into multi-label models. Notably, however, these approaches seldom pay attention to the label correlation and propagation in the feature subspace. To address this issue, we introduce an alternative multi-label feature learning solution that incorporates both labeled and unlabeled information. Unlike existing multi-label learning models , which rely on clean and trustworthy training datasets, we argue that in semi-supervised learning scenarios, the unlabeled data can be easily corrupted by noise or outliers, which causes the model performance to degrade. We next extract the label correlation and propagate the label information to discover the noise or outliers. Subsequently, our model adaptively searches the optimal feature subspace to reduce the influence of redundant features for high-dimensional data. The effectiveness of the proposed model is demonstrated by experimental observations on artificial and real-world datasets.