Robust local metric learning via least square regression regularization for scene recognition

Metric learning plays an important role in various machine learning tasks. Particularly, local metric learning is prevailing since it can learn more flexible metrics on complex datasets. However it may not be robust for scene recognition because of intra-class diversity and inter-class similarity in the scene images. To address this issue, we propose a novel method, called robust local metric learning via least square regression regularization (RLML-LSR), to learn a more robust distance metric for scene recognition. We first formulate a local discriminative metric function, aimed to pull same class neighbors closer and push different classes ones farther away simultaneously. Then taking advantage of the least square regression, we minimize the regression errors of same class neighbors, such that the local geometry structure can be preserved as much as possible. Finally, the local discriminative metric function and least square regression regularization are integrated into a unified framework, which jointly promotes the robustness of local metric learning and enhances the recognition performance of scene images. Extensive experiments on both natural scene and remote sensing scene datasets demonstrate the effectiveness and robustness of the proposed RLML-LSR method for scene recognition. (c) 2020 Elsevier B.V. All rights reserved.