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Open AccessArticle

Collaborative Representation Using Non-Negative Samples for Image Classification

PAMI Research Group, Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macau 999078, China
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Sensors 2019, 19(11), 2609; https://doi.org/10.3390/s19112609
Received: 6 May 2019 / Revised: 4 June 2019 / Accepted: 4 June 2019 / Published: 8 June 2019
Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using l 2 regularization, the collaborative representation based classifier holds competitive performances compared with the sparse representation based classifier using less computational time. However, each of the elements calculated from the training samples are utilized for representation without selection, which can lead to poor performances in some classification tasks. To resolve this issue, in this paper, we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC). To collect all non-negative collaborative representations, we introduce a Rectified Linear Unit (ReLU) function to perform filtering on the coefficients obtained by l 2 minimization according to CRC’s objective function. Next, we represent the test sample by using a linear combination of these representations. Lastly, the nearest subspace classifier is used to perform classification on the test samples. The experiments performed on four different databases including face and palmprint showed the promising results of the proposed method. Accuracy comparisons with other state-of-art sparse representation-based classifiers demonstrated the effectiveness of NCRC at image classification. In addition, the proposed NCRC consumes less computational time, further illustrating the efficiency of NCRC. View Full-Text
Keywords: collaborative representation-based classification; non-negative samples; image classification collaborative representation-based classification; non-negative samples; image classification
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Zhou, J.; Zhang, B. Collaborative Representation Using Non-Negative Samples for Image Classification. Sensors 2019, 19, 2609.

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