Fadi Dornaika
Abstract
The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label). The two types of class-wise reconstruction errors are blended in order to decide the class of any test image. Experiments conducted on four public image datasets show that the proposed ATPCRC can outperform the classic TPTSSR as well as many state-of-the-art methods that exploit label and unlabeled data samples.
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Index Terms
- Active Two Phase Collaborative Representation Classifier
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