Adaptive Subspace Incremental PCA based Online Learning for Object Classification and Recognition
The learning method for visual object 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 object 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 object, realizing online object knowledge accumulation and updating, and improving the recognition performance of system.
Object Recognition Online Learning Online PCA Adaptive Subspace
Xinyu Qu Minghai Yao
College of Information Technology Zhejiang University of Technology Hangzhou, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1515-1519
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）