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稿件标题: Integrating absolute distances in collaborative representation for robust image classification
稿件作者: Shaoning Zeng, Xiong Yang, Jianping Gou, Jiajun Wen
关键字词: Sparse representation; Collaborative representation; Integration; Image classification; Face recognition
文章摘要: Conventional sparse representation based classification (SRC) represents a test sample with the coefficient solved by each training sample in all classes. As a special version and improvement to SRC, collaborative representation based classification (CRC) obtains representation with the contribution from all training samples and produces more promising results on facial image classification. In the solutions of representation coefficients, CRC considers original value of contributions from all samples. However, one prevalent practice in such kind of distance-based methods is to consider only absolute value of the distance rather than both positive and negative values. In this paper, we propose an novel method to improve collaborative representation based classification, which integrates an absolute distance vector into the residuals solved by collaborative representation. And we named it AbsCRC. The key step in AbsCRC method is to use factors a and b as weight to combine CRC residuals
收录刊物: 2016年1卷2期
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