视觉分类的自适应加权多元素协同表示

Ganglong Duan, Long Wei, Jianren Wang, Hongqi Wang
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引用次数: 0

摘要

提出了一种用于视觉分类的自适应加权多元素协同表示方法。针对SRC(稀疏表示分类器)方法识别能力弱的问题,提出用多个元素表示每个元素,构建多个协同表示进行分类。为了反映具有不同重要性和判别能力的不同元素,提出了一种自适应加权残差法,将不同元素表示线性组合进行分类。实验结果证明了该方法的有效性和较好的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Weighted Multi-Element Collaborative Representation for Visual Classification
Adaptive Weighted Multi-Element Collaborative Representation for Visual Classification is proposed in this paper. To address the weak discriminative power of SRC (sparse representation classifier) method, we propose using multiple elements to represent each element and construct multiple collaborative representation for classification. To reflect the different element with different importance and discriminative power, we present an adaptive weighted residuals method to linearly combine different element representations for classification. Experimental results demonstrate the effectiveness and better classification accuracy of our proposed method.
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