摘要
多观测样本分类问题中,同一对象的多观测样本均看作一个整体进行识别,其同等看待各个观测样本。考虑到其每个观测样本包含判别信息量不同,针对如何有效利用其可信度问题,提出基于观测样本联合加权稀疏表示多观测样本分类算法。首先将多观测样本分解成单样本,分别对各个样本进行稀疏求解得到其各自的稀疏度和残差,进而联合二者确定其相应可信度。然后给各观测样本进行可信度加权,重构出加权多观测样本。最后,再采用整体稀疏表示对其进行分类。在ETH-80物体数据库、CMU-PIE人脸数据库和BANCA数据库上进行大量对比实验,实验结果证明该算法的有效性,提高识别精度的同时使算法的鲁棒性得到保证。
In classification problem of multiple observation sets,multiple observations of the same object were viewed as a whole for recognition,which respect all single samples equally.Considering the different amount of discriminative information from each single sample,with regard to how to exploit their respective different discriminant information,multiple observation sets classification algorithm based on joint sample weighted sparse representation is proposed.Multiple observation sets are first divided into single samples and each sample is processed separately by a sparse solution,obtained its respective sparsity and residual,then use them jointly to obtain its different reliability.After that,each single sample is weighted by corresponding reliability,the weighted multiple observation sets are reconstructed.Lastly,the classification is completed by global sparse representation.Extensive comparative experiments are conducted on ETH-80 object dataset,CMU-PIE face dataset and BANCA datasets,the results verify the effectiveness of the proposed algorithm,enhancing the recognition accuracy and the robust of the algorithm.
出处
《信号处理》
CSCD
北大核心
2014年第4期413-421,共9页
Journal of Signal Processing
基金
国家自然科学基金(61071199)
河北省自然科学基金(F2010001297)
关键词
多观测样本分类
联合稀疏表示
重构样本
可信度加权
判别信息
multiple observation sets classification
joint sparse representation
reconstructed samples
reliability weighted
discriminative information
