A TENSORFLOW BASED METHOD FOR LOCAL DERIVATIVE PATTERN

Abstract

Python programming language provides a very convenient environment of implementing machine learning applications. However, programmers usually faced with a poor performance compared to compiled functions when they write script based programs that demands intense computations. TensorFlow framework provides acceleration by enabling the utilization of various computing resources such as multicore CPU and GPU unit as well as including various compiled algorithms for developing machine learning applications. In this way, algorithms developed using existing TensorFlow operations can shorten computation times by using these resources indirectly without requiring parallel programming or GPU programming. In this study, Local Derivative Pattern (LDP) analysis which is one of the efficient feature extraction approaches for machine learning models was realized using a TensorFlow based algorithm. Independent pixel based operations in LDP algorithm which requires intense computations, enable developing an efficient tensor based algorithm. The performance of the TensorFlow based algorithm has been measured by comparing it with the Python script version of the same algorithm. The results obtained for various sizes and numbers of sample images show that TensorFlow operations provide significant acceleration rates for the LDP algorithm.

Keywords

• TensorFlow
• Local Derivative Pattern
• Python

References

1. Zhang , B., Gao, Y., Zhao, S. and Liu, J., “Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor,” IEEE Trans. Image Process., vol. 19, no. 2, pp. 533–544, 2010.
2. Lee, E. C., H. Jung and Kim, D., “New finger biometric method using near infrared imaging,” Sensors, vol. 11, no. 3, pp. 2319–2333, 2011.
3. Srivastava,G. and Srivastava, R., “Annotation of images using local binary pattern and local derivative pattern after salient object detection using minimum directional contrast and gradient vector flow,” Signal, Image Video Process., pp. 1–9, 2020.
4. Imani, Z. and Soltanizadeh, H., “Local Binary Pattern, Local Derivative Pattern and Skeleton Features for RGB-D Person Re-identification,” Natl. Acad. Sci. Lett., vol. 42, no. 3, pp. 233–238, 2019.
5. Darapureddy, N., N. and Karatapu, Battula, T. K., “Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization,” Int. J. Imaging Syst. Technol., p. e22475, 2020.
6. Jiang, D., Shi, Y., Chen, X., Wang, M. and Song, Z., “Fast and robust multimodal image registration using a local derivative pattern:,” Med. Phys., vol. 44, no. 2, pp. 497–509, 2017.
7. Kalam, A., Hasan, M., Enamul Haque, M., Ibrahim, M., Jashem, M. and Jabid, T., “Facial expression recognition using local composition pattern,” in ACM International Conference Proceeding Series, 2019, pp. 63–67.
8. Soltanpour, S. and Wu, Q. M. J., “Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition,” IEEE Trans. Image Process., vol. 28, no. 6, pp. 3020–3033, 2019.

9. Kwon, O. S., “Illuminant-invariant face recognition using high-order local derivative pattern,” J. Imaging Sci. Technol., vol. 62, no. 1, p. 10501, 2018.
10. Liang, J., Zhou, J. and Gao, Y. “3D local derivative pattern for hyperspectral face recognition,” in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015, 2015, vol. 1, pp. 1–6.
11. Abadi, M. et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv Prepr. arXiv1603.04467, 2016.
12. Abadi, M. et al., “TensorFlow: A system for large-scale machine learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, 2016, pp. 265–283.
13. Chien, S. W. D., Markidis, S., V. Olshevsky, Bulatov, Y., E. Laure and Vetter, J., “TensorFlow Doing HPC,” in 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2019, pp. 509–518.
14. Agrawal, A. et al., “TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning,” arXiv Prepr. arXiv1903.01855, 2019.
15. Andrade-Loarca, H. and Kutyniok, G., “tfShearlab: The TensorFlow Digital Shearlet Transform for Deep Learning,” arXiv Prepr. arXiv2006.04591, 2020.
16. Pietikäinen, M., Hadid, A., Zhao, G. and Ahonen, T., “Local Binary Patterns for Still Images,” Springer, pp. 13–47, 2011.
17. Zhao, Y., Ding, Y. and Zhao, X. Y., “Image quality assessment based on complementary local feature extraction and quantification,” Electron. Lett., vol. 52, no. 22, pp. 1849–1851, 2016.
18. Gomathy Nayagam, M. and Ramar, K., “Reliable object recognition system for cloud video data based on LDP features,” Comput. Commun., vol. 149, pp. 343–349, 2020.
19. Deng, J., Dong, W., Socher, R., Li, L.J., Li, Kai and Fei-Fei, Li, “ImageNet: A large-scale hierarchical image database,” IEEE conference on computer vision and pattern recognition, 2010, pp. 248–255.