Abstract
Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using 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 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.
Highlights
Image classification techniques have been extensively researched in computer vision [1,2,3,4,5]
This paper proposes a novel collaborative representation based classification method, named Non-negative Collaborative representation based classification (CRC) (NCRC)
We propose a novel image classification algorithm using non-negative samples based on the collaborative representation based classifier
Summary
Image classification techniques have been extensively researched in computer vision [1,2,3,4,5]. Sparse representation based classification methods [6] and its variants are frequently proposed and refined due to their effectiveness and efficiency, especially in face recognition [7,8,9]. Rather than using sparse representation (SR), the collaborative representation based classifier was proposed by using collaborative representation (CR), which achieved competitive performances with higher efficiency. Many applications have shown that both methods provide good results in image classification [5,10,11,12,13], where they can be further improved for a better recognition performance. Timofte et al imposed weights on the coefficients of collaborative representation [14] and achieved better performances in face recognition. Fan et al [8] provided another weights-imposing method, which derives weights of each coefficient
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