CAJ | 학술논문

提出了一种基于多特征字典的稀疏表示算法。该算法针对SRC的单特征鉴别性较弱这一不足，对样本提出多个不同特征并分别进行相应的稀疏表示。并根据SRC算法计算各个特征的鉴别性，自适应地学习出稀疏权重并进行线性加权，从而提高分类的性能。实验表明，基于自适应权重的多重稀疏表示分类算法，具有更好的分类效果。
제출료일충기우다특정자전적희소표시산법。해산법침대SRC적단특정감별성교약저일불족，대양본제출다개불동특정병분별진행상응적희소표시。병근거SRC산법계산각개특정적감별성，자괄응지학습출희소권중병진행선성가권，종이제고분류적성능。실험표명，기우자괄응권중적다중희소표시분류산법，구유경호적분류효과。
An adaptive weighted multiple sparse representation classification method is proposed in this paper. To address the weak discriminative power of the conventional SRC(Sparse Representation Classifier)method which uses a single feature representation, it proposes using multiple features to represent each sample and construct multiple feature sub-dictionaries for classification. To reflect the different importance and discriminative power of each feature, it presents an adaptive weighted method to linearly combine different feature representations for classification. Experimental results demonstrate the effectiveness of the proposed method and better classification accuracy can be obtained than the conven-tional SRC method.