br Results br Conclusion br Acknowledgement This work was supported

Results

Conclusion

Acknowledgement
This work was supported in part by the Natural Science Foundation of China under Grant No.61471216 and in part by the Special Foundation for the Development of Strategic Emerging Industries of Shenzhen under Grant No.JCYJ20150831192224146 and No.JCYJ20150601165744635.

Introduction
Image classification is a significant branch of computer vision. In this branch, the representation based classification methods have attracted considerable attention. A good representation for target images is greatly beneficial to improve the performance of image classification [1,2]. An object can be distinguished from the others when its image is well represented by the other images from this object. The combination of multiple representations of images is an effective method to improve the performance of representation based methods [3,4]. Therefore, it glibenclamide is an important and meaningful topic to find a proper representation for representation based image classification methods.
At present, face recognition has been studied widely and many useful methods have been presented [5–9]. However, we still face with some great challenges. Different poses and expressions, various intensities of illuminations and insufficient training samples seriously influence the recognition effects. In order to address these challenges, people have made many efforts. For various illuminations, by handling the original images to enhance pixels with moderate intensities of the original images and reduce the importance of other pixels, Xu et al. [10] obtained the complementary images to improve the accuracy of image classification. Producing the mirror image of the face and integrating the original face image and its mirror image are also useful to improve the recognition accuracy of representation-based face recognition [11]. For the problem of insufficient training samples, Huang et al. [12] proposed a robust kernel collaborative representation classification method based on virtual samples for face recognition to reduce the influence of insufficient training samples. The use of symmetrical face images generated from original face images is very useful to overcome the problem of varying appearances of faces [13,14]. Until now, many works focus on generating virtual or synthesized face images to enhance the recognition accuracy [15–19]. The simultaneous use of original face images and their virtual face images can improve the accuracy of face recognition. What is more, several works have shown that virtual image obtained by exploiting the adjacent rows of original image are also useful for image classification [20–24].
Wright et al. [25] proposed the sparse representation classification (SRC) algorithm which can reach satisfactory result. There are many SRC algorithms [26–30]. However, the original SRC algorithm with the constraint of l minimization is time consuming. Zhang et al. [31] proved that the essence to obtain the satisfactory performance of the SRC algorithm is the collaborative representation but not the sparsity, and proposed a collaborative representation classification (CRC) method with the constraint of l minimization. CRC methods can obtain comparative performance to SRC algorithm, but is much faster than SRC algorithm. Various representation methods with the constraints of l minimization are also proposed, such as linear regression classification (LRC) [32], and two phase sparse representation [33–35]. They not only used simple constraint conditions but also achieved satisfactory recognition accuracy.
The remainder of Helper virus paper is organized as follows. Section 2 presents the proposed novel representation method of images. Section 3 describes the underlying rationale of the proposed method. Section 4 shows the experimental results. Section 5 provides the conclusions of this paper.

The proposed method