.png){kind=icon}
返回
Local Kernel Feature Analysis (LKFA) for object recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2010.09.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method. (c) 2010 Elsevier B.V. All rights reserved.
Keyword:
Local
Kernel
Biometric
Face
Palmprint
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
Sodium‐Ion Batteries: Prussian Blue Cathode Materials for Sodium‐Ion Batteries and Other Ion Batteries (Adv. Energy Mater. 17/2018)锂离子电池: 用于锂离子电池和其他离子电池的普鲁士蓝正极材料 (Adv. Energy Mater. 17/2018)
OAAI
OAAI分享收藏分享收藏
查看更多内容