Joint global metric learning and local manifold preservation for scene recognition

Abstract

Scene recognition plays an important role in many computer vision tasks. However, the recognition performance hardly meets the development of computer vision, since scene images show large variations in spatial position, illumination, and scale. To address this issue, a joint global metric learning and local manifold preservation (JGML-LMP) approach is proposed. First, we formulate a new global metric learning problem based on the cluster centers of each specific class, allowing to capture the global discriminative information with more informative samples. Second, in order to exploit the local manifold structure, we introduce an adaptive nearest neighbors constraint through which the local intrinsic relationships can be preserved in the new metric space instead of the Euclidean space. Third, through performing global metric learning and local manifold preservation jointly within a unified optimization framework, our approach can take advantage of both global and local information, and hence produces more discriminative and robust feature representations for scene recognition. Extensive experiments on four benchmark scene datasets demonstrate the superiority of the proposed method over state-of-the-art methods.