International Journal of Nonlinear Analysis and Applications

Improving image segmentation using artificial neural networks and evolutionary algorithms

Document Type : Research Paper

Authors

1 Faculty of Computer Engineering, Shomal University, Amol 46161-84596, Mazandaran, Iran

2 Faculty of Computer Engineering Department, Shahrood University of Technology, Shahrood, Semnan, Iran

Abstract

Image segmentation can be used in object recognition systems. Today, it is considered in most branches of science and industry, and in many of these branches the identification of the main components of the image is very important. For example, automatic detection and tracking of moving targets in military applications and segregation of different products in industrial applications, identification of road signs, segmentation of colonies, land use and land cover classification. It is also widely used in medicine, such as diagnosing brain and tumors and self-driving. In this study, image sections are performed by a feature extraction process using a neural network. In the process of applying the neural network method, optimization was performed using the ant colony algorithm. The results show that the identification of image segments using the neural network has an accuracy of 87% alone, but increased to 90% after optimization using ant colony optimization.

Keywords

[1] M. Analoui and M. Fadavi Amiri, Feature reduction of nearest neighbor classifiers using genetic algorithm, Proc. World Acad. Sci. Engin. Technol. 17 (2006), 36–39.
[2] V. Badrinarayanan, A. Kendall and R. Cipolla, SegNet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017), no. 12, 2481–2495.
[3] G.J. Brostow, J. Shotton, J. Fauqueur and R. Cipolla, Segmentation and Recognition Using Structure from Motion Point Clouds, Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg, 2008.
[4] L. Chen, J. Barron, G. Papandreou, K. Murphy and A. Yuille, Semantic image segmentation with task-specific edge detection using CNNS and a discriminatively trained domain transform, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2016, pp. 4545–4554.
[5] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A.L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Machine Intell. 40 (2017), no. 4, 834–848.
[6] D. Ciresan, A. Giusti, L.M. Gambardella and J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images, Adv. Neural Inf. Process. Syst. 25 (2012), 2843–2851.
[7] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth and B. Schiele, The cityscapes dataset for semantic urban scene understanding, Proc. IEEE Conf. Comput. Vision Pattern Recog. (CVPR), 2016, pp. 3213–3223.
[8] A. Ess, T. Muller, G. Grabner and L.J. Van Gool, Segmentation-based urban traffic scene understanding, BMVC 1 (2009).
[9] C. Farabet, C. Couprie, L. Najman and Y. LeCun, Learning hierarchical features for scene labelling, IEEE Trans. Pattern Anal. Machine Intell. 35  (2013), no. 8, 1915–1929.
[10] A. Geiger, P. Lenz and R. Urtasun, Are we ready for autonomous driving? the Kitti vision benchmark suite, IEEE Conf. Comput. Vision Pattern Recog., 2012, pp. 3354–3361.
[11] Y. Gordienko, P. Gang, J. Hui, W. Zeng, Y. Kochura, O. Alienin, O. Rokovyi and S. Stirenko, Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer, Int. Conf. Theory Appl. Fuzzy Syst. Soft Comput., Springer, 2018, pp. 638–647.
[12] S. Gupta, R. Girshick, P. Arbel´aez and J. Malik, Learning rich features from RGB-D images for object detection and segmentation, Computer Vision–ECCV 2014: 13th Eur. Conf., Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VII 13, Springer International Publishing, 2014, pp. 345–360.
[13] P. Hariharan, P. Arbelae, R. Girshick and J. Malik, Simultaneous detection and segmentation, Eur. Conf. Comput. Vision, Springer, 2014, pp. 297–312.
[14] M. Hashemi, H. Hassanpour, E. Kozegar and T. Tan, Cystoscopy image classification using deep convolutional neural networks, Int. J. Nonlinear Anal. Appl. 10 (2019), no. 1, 193–215.
[15] M. Hashemi, H. Hassanpour, E. Kozegar and T. Tan, Cystoscopic image classification based on combining MLP and GA, Int. J. Nonlinear Anal. Appl. 11 (2019), no. 1, 93–105.
[16] K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2016, pp. 770–778.
[17] A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25 (2012), 1097–1105.
[18] H. Li, P. Xiong, H. Fan and J. Sun, Dfanet: Deep feature aggregation for real-time semantic segmentation, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2019, pp. 9522–9531.
[19] J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 79 (2014), no. 10, 1337–1342.
[20] M. Mostajabi, P. Yadollahpour, G. Shakhnarovich and A. Feedforward, Semantic segmentation with zoom-out features, Proc. Comput. Vision Pattern Recog., 2015, pp. 3376–3385.
[21] F. Ning, D. Delhomme, Y. LeCun, F. Piano, L. Bottou and E.P. Barbano, Toward automatic phenotyping of developing embryos from videos, IEEE Trans. Image Process. 14 (2005), no. 9, 1360–1371.
[22] M. Oberweger, P. Wohlhart and V. Lepetit, Hands deep in deep learning for hand pose estimation, arXiv preprint arXiv: 1502.06807. (2015).
[23] M. Orsi, I. Kreso, P. Bevandic and S. Segvic, In defence of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2019, pp. 12607–12616.
[24] A. Paszke, A. Chaurasia, S. Kim and E. Culurciello, ENet: A deep Neural Network Architecture for Real-Time Semantic Segmentation, 7 Jun 2016, in Computer Vision and Pattern Recognition (cs.CV) arXiv:1606.02147 [cs.CV]. 2016.
[25] P. Pirozmand, M. Fadavi Amiri, F. Kashanchi and N. Yugeeta Layne, Age estimation, a Gabor PCA-LDA approach, J. Math. Comput. Sci. 2 (2011), no. 2, 233–240.
[26] O. Ronneberger, P. Fischer and T. Brox, U-net: convolutional networks for biomedical image segmentation, Int. Conf. Med. Image Comput. Comput.-Assis. Interven., Springer, 2015, pp. 234–41.
[27] K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556, 2014.
[28] K. Socha and M. Dorigo, Ant colony optimization for continuous domains, Eur. J. Oper. Res. 185 (2005), no. 3, 1155–1173.
[29] P. Sturgess, K. Alahari, L. Ladicky and P.H.S. Torr, Combining appearance and structure from motion features for road scene understanding, BMVC-British Machine Vision Conf., BMVA, 2009.
[30] W. Szegedy, Y. Liu, P. Jia, S. Sermanet, D. Reed, D. Anguelov, V. Erhan, V. Vanhoucke and A. Rabinovich, Going deeper with convolutions, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2015, pp. 1–9.
[31] J. Tighe and S. Lazebnik, SuperParsing: Scalable nonparametric image parsing with superpixels, Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010, Lecture Notes in Computer Science, vol 6315, Springer, Berlin, Heidelberg, 2010.
[32] F. Visin, M. Ciccone, A. Romero, K. Kastner, K. Cho, Y. Bengio, M. Matteucci and A. Courville, ReSeg: A recurrent neural network-based model for semantic segmentation, Proc. IEEE Conf. Comput. Vision Pattern Recog. Workshops, 2016, pp. 41–48.
[33] J. Wan, D. Wang, S.C.H. Hoi, P. Wu, J. Zhu, Y. Zhang and J. Li, Deep learning for content-based image retrieval: A comprehensive study, Proc. 22nd ACM Int. Conf. Multimedia, ACM, 2014, pp. 157—166.
[34] Y. Yang, Z. Li, L. Zhang, C. Murphy, J. Ver Hoeve and H. Jiang, Local Label Descriptor for Example-Based Semantic Image Labeling, Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012, Lecture Notes in Computer Science, vol 7578, Springer, Berlin, Heidelberg, 2012.
[35] Y. Yoon, H.G. Jeon, D. Yoo, J.Y. Lee and I. So Kweon, Learning a deep convolutional network for light-field image super-resolution, Proc. IEEE Int. Conf. Comput. Vision Workshops, 2015, pp. 24–32.
[36] C. Yu, J. Wang, C. Peng, C. Gao, G. Yu and N. Sang, Bisenet: Bilateral segmentation network for real-time semantic segmentation, Proc. Eur. Conf. Comput. Vision (ECCV), 2018a, pp. 325–341.
[36] C. Yu, J. Wang, C. Peng, C. Gao, G. Yu and N. Sang, Learning a discriminative feature network for semantic segmentation, Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2018b, pp. 1857–1866.
[38] M.D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014, Lecture Notes in Computer Science, vol 8689. Springer, Cham, 2014.
[39] C. Zhang, L. Wang and R. Yang, Semantic segmentation of urban scenes using dense depth maps, Computer Vision–ECCV 2010: 11th Euro. Conf. Comput. Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV 11. Springer Berlin Heidelberg, 2010, pp. 708–721.
[40] Y.J. Zhang, Influence of segmentation over feature measurement, Pattern Recog. Lett. 16 (1995), no. 2, 201–206.
[41] H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, Pyramid scene parsing network, Proc. IEEE Conf. Comput. Vision Pattern Recog., 2017, pp. 2881–2890.
[42] Sh. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Zh. Su, D. Du, Ch. Huang, and Ph.H.S. Torr, Conditional random fields as recurrent neural networks, On 11 Feb 2015 arXiv:1502.03240 [cs.CV].
International Journal of Nonlinear Analysis and Applications
Volume 15, Issue 3
March 2024
Pages 125-140
  • Receive Date: 19 January 2023
  • Revise Date: 16 March 2023
  • Accept Date: 06 April 2023