Abstract
In the domains of pattern recognition and computer vision, sparse representation classifier and its variants are considered as powerful classifiers. However, due to the use of sparse coding in most of its variants, classifying test samples is computationally expensive. Thus, it is not practical for scenarios demanding fast classification. For this reason, a two-phase coding classifier based on classic regularized least square was proposed recently. A significant limitation of this classifier is the fact that the number of local bases that should be handed over to the next coding phase should be specified manually. This paper overcomes this main limitation and proposes three data-driven schemes allowing an automatic estimation of the optimal size of the local bases. Experiments conducted on five image datasets show that the introduced schemes, despite their simplicity, can improve the performance of the two-phase linear coding classifier adopting ad hoc choices for the number of local bases.
Similar content being viewed by others
References
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2(1), 183–202 (2009)
Dornaika, F., Bosaghzadeh, A.: Adaptive graph construction using data self-representativeness for pattern classification. Inf. Sci. 325, 118–139 (2015)
Dornaika, F., Dhabi, R., Bosaghzadeh, A., Ruichek, Y.: Efficient dynamic graph construction for inductive semi-supervised learning. Neural Netw. 94, 192–203 (2017)
He, R., Zheng, W., Hu, B., Kong, X.: Two-stage nonnegative sparse representation for large-scale face recognition. IEEE Trans. Neural Netw. Learn. Syst. 24, 35–46 (2013)
Koc, A., Bartan, B., Gundogdu, E., Cukur, T., Ozaktas, H.M.: Sparse representation of two- and three-dimensional images with fractional fourier, hartley, linear canonical, and haar wavelet transforms. Exp. Syst. Appl. 77, 247–255 (2017)
Lazebnik, S., Schmid, C., Ponce J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Vision and Pattern Recognition (2006)
Li, C., Guo, J., Zhang, H.: Local sparse representation based classification. In: IEEE International Conference on Pattern Recognition (2010)
Liu, Z., Pu, J., Huang, T., Qiu, Y.: A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl. Intell. 39, 407–414 (2013)
Ramli, D.A., Chien, T.W.: Fast kernel sparse representation classifier using improved smoothed L0 norm. In: International Conference on Knowledge Based and Intelligent Information and Engineering Systems (2017)
Shi, Q., Eriksson, A., Hengel, A., Shen, C.: Is face recognition really a compressive sensing problem?. In IEEE Int. Conf, CVPR (2011)
Waqas, J., Yi, Z., Zhang, L.: Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recognit. Lett. 34, 201–208 (2013)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Xiang, W., Wang, J., Long, M.: Local hybrid coding for image classification. In: IEEE International Conference on Pattern Recognition (2014)
Xu, Y., Li, Z., Yang, J., Zhang, D.: A survey of dictionary learning algorithms for face recognition. IEEE Access 5, 8502–8514 (2017)
Xu, Y., Zhang, D., Yang, J., Yang, J.-Y.: A two-phase test sample sparse representation method for use with face recognition. IEEE Trans. Circuits Syst. Video Technol. 21(9), 1255–1262 (2011)
Yang, A., Sastry, S., Ganesh, A., Ma, Y.: Fast \(\ell_1\)-minimization algorithms and an application in robust face recognition: a review. In IEEE ICIP (2010)
Yang, M., Zhang, L., Yang, J., Zhang, D.: Robust sparse coding for face recognition. In: IEEE International Conference on Computer Vision Pattern Recognition (2011)
Zhang, H., Lei, Q., Zhong, B., Du, J., Chen, D.: A sparse representation algorithm for effective photograph retrieval. Math. Comput. Appl. 22, 8 (2017)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: International Conference on Computer Vision (2011)
Zhang, L., Yang, M., Feng, X., Ma, Y., Zhang, D.: Collaborative representation based classification for face recognition. arXiv:1204.2358 (2012)
Zhou, Z., Feng, J.: Deep forest: towards an alternative to deep neural networks. arXiv:1702.08835v2 (2017)
Zhu, Q., Yuan, N., Guan, D., Xu, N., Li, H.: An alternative to face image representation and classification. Int. J. Mach. Learn. Cybern. (2018)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dornaika, F., El Traboulsi, Y. Proposals for local basis selection for the sparse representation-based classifier. SIViP 12, 1595–1601 (2018). https://doi.org/10.1007/s11760-018-1316-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-018-1316-7