判别式
公制(单位)
人工智能
计算机科学
模式识别(心理学)
歧管(流体力学)
水准点(测量)
特征(语言学)
欧几里得空间
约束(计算机辅助设计)
度量空间
特征向量
非线性降维
机器学习
数学
降维
地理
纯数学
哲学
经济
几何学
数学分析
工程类
机械工程
语言学
运营管理
大地测量学
作者
Chen Wang,Guohua Peng,Bernard De Baets
标识
DOI:10.1016/j.ins.2022.07.188
摘要
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.
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