Skip to main content
SpringerLink
Log in

An efficient method of multicolor detection using global optimum thresholding for image analysis

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image segmentation is a key step in the image analysis, pattern recognition, low-level vision, medical data analysis, objects tracking, recognition task and grasping of things from the field of robotics. Being a problematic and demanding chore in image processing, it governs the eminence of absolute outcomes of image analysis. The method aims to improve color detection using formulations in RGB arrays. First targeted color is selected and identified the desired color location by sliding window techniques. Then threshold has been calculated using the summation of within and between the class variance of the selected color. Proposed method overcomes the limitation of complex, the dearth incorrectness, and steadiness of conventional multilevel thresholding for image segmentation. This work is tested on a different kind of images such as two-dimensional images, low-quality images, complex images, blur images, and medical images. The simulated results designate the maximum accuracy and minimum computational time over other methods.

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
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Alsultanny YA (2010) Color image segmentation to the RGB and HSI model based on region growing algorithm. Recent Advances in Computer Engineering and Applications 63–684

  2. Arora S, Acharya J, Verma A, Panigrahi PK (2008) Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recognit Lett 29(2):119–125

    Article  Google Scholar 

  3. Batavia PH, Singh S (2001) Obstacle detection using adaptive color segmentation and color stereo homography. In: Proceeding of IEEE International Conference on Robotics & Automation, pp. 705–710

  4. Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 94(2):115–147

  5. Chavolla E, Valdivia A, Diaz P, Zaldivar D, Cuevas E, Perez MA (2018) Improved unsupervised color segmentation using a modified HSV color model and a bagging procedure in K-means ++ algorithm. Math Probl Eng 2018:1–23

    Article  Google Scholar 

  6. Chen J, Pappast TN (2002) Adaptive image segmentation based on color and texture. In: IEEE international Conference on Image Processing, pp. 777–780

  7. Chiu KY, Lin SF (2005) Lane detection using color-based segmentation. In: IEEE Proceedings Intelligent Vehicles Symposium 706–711

  8. Cortes MAD, Cuevas E, Rojas R (2017) Color segmentation using LVQ neural networks. In: Engineering Applications of Soft Computing 59–74

  9. Hassan MR, Ema RR, Islam T (2017) Color image segmentation using automated K-means clustering with RGB and HSV color spaces. Global Journal of Computer Science and Technology F Graphics and Visio 17(2):32-41

  10. Ghamisi P, Couceiro MS, Benediktsson JA (2013) Classification of hyperspectiral images with binary fractional order drawinian PSO and random forests. Image Signal Process Remote Sens 8892:1–8

    Google Scholar 

  11. Ghamisi P, Couceiro MS, Martins FML, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Transactions on geoscience and remote sensing 52(5): 2382-2394

  12. Goel V, Singhal S, Kole S, Jain T (2017) Specific Color Detection in Images using RGB Modelling in MATLAB. Int J Comput Appl 161(8):38–42

    Google Scholar 

  13. Gothwal R, Gupta S, Gupta D, Dahiya AK (2014) Color image segmentation algorithm based on RGB channels. In: IEEE, International Conference on Reliability, Infocom Technologies and Optimization, pp. 1–5

  14. He Y, Wang H, Zhang B (2004) Color-Based Road Detection in Urban Traffic Scenes. IEEE Trans Intell Transp Syst 5(4):309–318

    Article  Google Scholar 

  15. Hyams J, Powell MW, Murphy R (2000) Cooperative Navigation of Micro-Rovers Using Color Segmentation. J Auton Robot 9(1):7–16

    Article  Google Scholar 

  16. Kaur M, Sharma R (2015) Quality detection of fruits by using ANN technique. IOSR J Electron Commun Eng 10(4):35–41

    Google Scholar 

  17. Marr D, Nishihara HK (1978) Representation and Recognition of the Spatial Organization of Three-Dimensional Shapes. Proc R Soc London 200(1140):269–294

    Google Scholar 

  18. Meunie J, Benalla M (2003) Real-time color segmentation of road signs. IEEE:1823–1826

  19. Milottaa FLM, Furnaria G, Quattrocchi C, Pasquale S, Allegra D, Gueli AM, Stanco F, Tanasi D (2020) Challenges in automatic Munsell color profiling for cultural heritage. Pattern Recognit Lett 131:135–141

    Article  Google Scholar 

  20. Safuan SNM, Tomari R, Zakaria WNW, Othman NB (2018) White blood cell (WBC) counting analysis of blood smear images using various color segmentation strategies. Measurement 116:543-555

  21. Ng HP, Ong SH, Goh PS, Nowinski WL (2006) Medical image segmentation using K-means clustering and improved watershed algorithm. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 61–65

  22. Nummiaro K, Koller-meier E, Van Gool L (2002) Object Tracking with an Adaptive Color-Based Particle Filter. Springer-Verlag, Heidelberg, pp 353–360

    MATH  Google Scholar 

  23. Palus H, Bereska D (2007) Region- based colour mage segmentation. In: International Conference on Machine Learning and Cybernetics, pp. 1–7

  24. Pinho TM, Coelho JP, Oliveira J, Cunha JB (2017) Comparative analysis between LDR and HDR images for automatic fruit recognition and counting. Journal of Sensors (6):1–12

  25. Pujol FA, Pujol M, Jimeno-Morenilla A, Pujol MJ (2017) Face detection based on skin color segmentation using fuzzy entropy. Entropy 19(26):1–22

    Google Scholar 

  26. Rahmat RF, Chairunnisa T, Gunawan D, Sitompul OS, Gunawan D, Sitompul OS (2016) Skin color segmentation using multi-color space threshold. In: International Conference On Computer And Information Sciences, pp. 391–396

  27. Rajinikanth V, Couceiro MS (2015) RGB histogram based color image segmentation using firefly algorithm. In: Elsevier, International Conference on Information and Communication Technologies, vol. 46, pp. 1449–1457

  28. Rajinikanth V, Raja NSM, Latha K Optimal multilevel image thresholding : an analysis with PSO and BFO algorithms. Int Eng 8:443–454

  29. Ramaraj M, Niraimathi S (2017) Application of color based image segmentation paradigm on rgb color pixels using fuzzy c-means and k means algorithms. Int J Comput Sci Mob Comput 6(6):430–440

    Google Scholar 

  30. Raval K, Shukla R, Shah AK (2017) Color image segmentation using FCM Clustering Technique in RGB, L * a * b , HSV , YIQ Color spaces. Eur J Adv Eng Technol 4(3):194–200

  31. Srivastava DK, Budhraja T (2016) An effective model for face detection using R, G, B color segmentation with genetic algorithm. In: Smart Innovation, Systems and Technologies 51:47–55

  32. Su Q, Hu Z (2013) Color image quantization algorithm based on self-adaptive differential evolution. Hindawi Publ Corp Comput Intell Neurosci 2013:1–8

    Article  Google Scholar 

  33. Sun T, Tsai S, Chan V, Overview A (2006) HSI color model based lane-marking detection. In: Proceedings of the IEEE Intelligent Transportation Systems Conference, pp. 1168–1172

  34. Tanaka J, Weiskopf D, Williams P (2001) The role of color in high-level vision. Trends Cogn Sci. 5(5):211–215

    Article  Google Scholar 

  35. Tremeau A, Borel N (1997) A region growing and merging algorithm to color segmentation. Pattern Recognit 30(7):1191–1203

    Article  Google Scholar 

  36. Verma OP, Hanmandlu M, Susan S, Kulkarni M, Jain PK (2011) A Simple Single Seeded Region Growing Algorithm for Color Image Segmentation using Adaptive Thresholding. In: IEEE, International Conference on Communication Systems and Network Technologies, pp. 1–4

  37. Wang J, Kong JUN, Lu Y, Gu W, Yin M, Xiao Y (2007) A region-based SRG algorithm for color image segmentation. In: Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, pp. 19–22

  38. Wu MN, Lin CC, Chang CC (2007) Brain tumor detection using color-based K-means clustering segmentation. In: Third international conference on intelligent information hiding and multimedia signal processing 2:245–250

  39. Xuan L, Mingjun Z (2017) Underwater color image segmentation method via RGB channel fusion. Opt Eng 56(2):1–13

    Article  Google Scholar 

  40. Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In: IEEE Conference on Computer Vision and Pattern Recognition 3166–3173

  41. Zhan Q, Liang Y, Xiao Y (2009) Color-based segmentation of point clouds. In: Bretar F, Pierrot-Deseilligny M, Vosselman G Laser scanning, IAPRS 38(3):248–252

  42. Zhenqiang Y, Ge L, Sixin W (2017) TyyGuozhen, “ORGB: Offset correction in RGB color space for illumination-robust image processing. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1557–1561

  43. Zohra F, Belahbib B, Souami F (2012) Color image segmentation by a genetic algorithm based clustering and connected component labeling. In: IEEE, International Conference on Microelectronics, no. Icm, pp. 1–4

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, India

    Lalit Mohan Goyal

  2. Department of CSE, G. B. Pant Government Engineering College, Okhla, New Delhi, India

    Mamta Mittal

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  4. University Institute of Computing, Chandigarh University, Mohali, Punjab, India

    Bhavneet Kaur & Meenakshi Sharma

  5. Department of Computer Science and Engineering, Chandigarh Group of Colleges, Chandigarh, Punjab, India

    Amit Verma & Iqbaldeep Kaur

Authors
  1. Lalit Mohan Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mamta Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bhavneet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Meenakshi Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amit Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Iqbaldeep Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Kumar.

Ethics declarations

Conflict of interest

Authors have no conflict of interest to declare.

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

Goyal, L.M., Mittal, M., Kumar, M. et al. An efficient method of multicolor detection using global optimum thresholding for image analysis. Multimed Tools Appl 80, 18969–18991 (2021). https://doi.org/10.1007/s11042-020-10365-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10365-y

Keywords

Search

Navigation