Skip to main content
SpringerLink
Log in

Hybrid optimization enabled deep learning model for colour image segmentation and classification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Image segmentation is one of the most significant tasks in image analysis, and it plays an imperative job in image processing to analyse and attain meaningful information. Moreover, image segmentation is a major process of object recognition and categorization in computer vision domain. Image segmentation utilizes the image features for separating images into definite areas along with exclusive properties. Meanwhile, various colour image segmentation techniques are introduced in computer vision research area. However, these techniques are more time consuming and failed to afford anticipated segmentation outcome, because of poor segmentation results and high computational difficulty. To overcome these challenges, an effectual hybrid optimization-based Deep Learning (DL) technique is devised for colour image segmentation and classification in this research study. The median filter is applied for input image to eliminate the noises, which assists for better image segmentation and classification process. Moreover, Improved Invasive Weed Flower Pollination Optimization (IIWFPO) approach is introduced for image segmentation process in this work. In addition, Deep Residual Network (DRN) classifier is employed for image classification, and the classifier is trained by developed Fr-IIWFPO algorithm. The developed colour image segmentation and classification approach obtained better performance than traditional techniques with accuracy of 0.9187, sensitivity of 0.9334, and specificity of 0.8902.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bora DJ (2018) Importance of image enhancement techniques in color image segmentation: a comprehensive and comparative study. arXiv preprint arXiv:1708.05081

  2. Feng L, Li H, Gao Y, Zhang Y (2020) A color image segmentation method based on region salient color and fuzzy c-means algorithm. Circuits Syst Signal Process 39(2):586–610

    Article  Google Scholar 

  3. Lei T, Jia X, Zhang Y, Liu S, Meng H, Nandi AK (2018) Superpixel-based fast fuzzy c-means clustering for color image segmentation. IEEE Trans Fuzzy Syst 27(9):1753–1766

    Article  Google Scholar 

  4. Xing Z, Jia H (2019) Multilevel color image segmentation based on GLCM and improved salp swarm algorithm. IEEE Access 7:37672–37690

    Article  Google Scholar 

  5. Liang H, Jia H, Xing Z, Ma J, Peng X (2019) Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7:11258–11295

    Article  Google Scholar 

  6. Bai M, Urtasun R (2017) Deep watershed transform for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5221–5229

  7. Ma J, Li S, Qin H, Hao A (2016) Unsupervised multi-class co-segmentation via joint-cut over $ L_ 1 $-manifold hyper-graph of discriminative image regions. IEEE Trans Image Process 26(3):1216–1230

    Article  MATH  Google Scholar 

  8. Yin S, Qian Y, Gong M (2017) Unsupervised hierarchical image segmentation through fuzzy entropy maximization. Pattern Recogn 68:245–259

    Article  Google Scholar 

  9. Gong M, Li H, Zhang X, Zhao Q, Wang B (2015) Nonparametric statistical active contour based on inclusion degree of fuzzy sets. IEEE Trans Fuzzy Syst 24(5):1176–1192

    Article  Google Scholar 

  10. Long J, Shelhamer E, Darrell, T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440

  11. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  12. Gangappa M, Kiran Mai C, Sammulal P (2019) Enhanced crow search optimization algorithm and hybrid NN-cnn classifiers for classification of land cover images. Multimed Res 2(3):12–22

    Google Scholar 

  13. Alagarsamy S, Kamatchi K, Govindaraj V, Thiyagarajan A (2017) A fully automated hybrid methodology using Cuckoo-based fuzzy clustering technique for magnetic resonance brain image segmentation. Int J Imaging Syst Technol 27(4):317–332

    Article  Google Scholar 

  14. Xing Z (2020) An improved emperor penguin optimization based multilevel thresholding for color image segmentation. Knowl-Based Syst 194:105570

    Article  Google Scholar 

  15. Rad AE, Rahim MSM, Kolivand H, Amin IBM (2017) Morphological region-based initial contour algorithm for level set methods in image segmentation. Multimed Tools Appl 76(2):2185–2201

    Article  Google Scholar 

  16. Zibetti MV, Helou ES, Pipa DR (2017) Accelerating overrelaxed and monotone fast iterative shrinkage-thresholding algorithms with line search for sparse reconstructions. IEEE Trans Image Process 26(7):3569–3578

    Article  MathSciNet  MATH  Google Scholar 

  17. Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, King BF, Torres VE, Erickson BJ (2017) Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. J Digit Imaging 30(4):442–448

    Article  Google Scholar 

  18. Bhandari AK (2020) A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Comput Appl 32(9):4583–4613

    Article  Google Scholar 

  19. Sowmya V, Govind D, Soman KP (2017) Significance of contrast and structure features for an improved color image classification system. In: Proceedings of 2017 IEEE international conference on signal and image processing applications (ICSIPA), pp. 210–215

  20. Yang XS (2012) Flower pollination algorithm for global optimization. In: Proceedings of international conference on unconventional computing and natural computation, pp. 240–249

  21. Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. J Comput Design Eng 6(3):284–295

    Article  Google Scholar 

  22. Bhaladhare PR, Jinwala DC (2014) A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Adv Comput Eng 2014:1

    Article  Google Scholar 

  23. Sun L, Luo B, Liu T, Liu Y, Wei Y (2019) Algorithm of adaptive fast clustering for fish swarm color image segmentation. IEEE Access 7:178753–178762

    Article  Google Scholar 

  24. Xu G, Zhou J, Dong J, Chen CP, Zhang T, Chen L, Han S, Wang L, Chen Y (2020) Multivariate morphological reconstruction based fuzzy clustering with a weighting multi-channel guided image filter for color image segmentation. Int J Mach Learn Cybern 11(12):2793–2806

    Article  Google Scholar 

  25. Chen Z, Chen Y, Wu L, Cheng S, Lin P (2019) Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Convers Manage 198:111793

    Article  Google Scholar 

  26. Narmadha RP, Sengottaiyan N, Kavitha RJ (2022) Deep Transfer Learning Based Rice Plant Disease Detection Model. Intell Autom Soft Comput 31(2):1257–1271

    Article  Google Scholar 

  27. Ananthi J, Sengottaiyan N, Anbukaruppusamy S, Upreti K, Dubey AK (2022) Forest fire prediction using IoT and deep learning. Int J Adv Technol Eng Explor 9(87):246

    Google Scholar 

  28. Stanford background dataset taken from. https://www.kaggle.com/balraj98/stanford-background-dataset, accessed on November 2021

  29. Deepa SN, Rasi D (2019) Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation. Soft Comput 23(8):2545–2559

    Article  Google Scholar 

  30. Rani KSK, Rasi D, Deepa SN (2018) Developed global biotic cross pollination algorithm for CIS. Int J Bus Intell Data Min 13(1–3):108–128

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, 641 032, India

    D. Rasi

  2. Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh Jote, Papum Pare District, Arunachal Pradesh, 791113, India

    S. N. Deepa

Authors
  1. D. Rasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. N. Deepa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. N. Deepa.

Ethics declarations

Conflict of interest

The authors confirm no conflict of interest in publishing this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rasi, D., Deepa, S.N. Hybrid optimization enabled deep learning model for colour image segmentation and classification. Neural Comput & Applic 34, 21335–21352 (2022). https://doi.org/10.1007/s00521-022-07614-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07614-6

Keywords

Search

Navigation