Adaptive Subspace based Online PCA Algorithm for Mobile Robot Scene Learning and Recognition
The learning method for visual scene recognition that compute a space of eigenvectors by Principal Component Analysis(PCA) traditionally require a batch computation step, in which the only way to update the subspace is to rebuild the subspace by the scratch when it comes to new samples. In this paper, we introduce a new approach to scene recognition based on online PCA algorithm with adaptive subspace, which allows for complete incremental learning. We propose to use different subspace updating strategy for new sample according to the degree of difference between new sample and learned sample, which can improve the adaptability in different situations, and also reduce the time of calculation and storage space. The experimental results show that the proposed method can recognize the unknown scene, realizing online scene accumulation and updating, and improving the recognition performance of system.
Scene Recognition Online Learning Online PCA Adaptive Subspace
Xinyu Qu Minghai Yao Qinlong Gu Jianfang Zhang
College of Information Technology Zhejiang University of Technology Hangzhou, China
国际会议
杭州
英文
205-209
2011-08-26（万方平台首次上网日期，不代表论文的发表时间）