Skip to main content

Advertisement

SpringerLink
Log in

Video superpixels generation through integration of curvelet transform and simple linear iterative clustering

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

Abstract

Superpixel generation finds wide variety of applications in the field of image processing, particularly brain tumour detection, human body pose estimation, person re-identification as a pre-processing step. In this paper, we present a novel superpixel segmentation approach through integration of curvelet transform and conventional Simple Linear Iterative Clustering (SLIC) for image and video. The proposed algorithm follows a two-step framework, clustering in curvelet and spatial domain separately. It performs conventional Simple Linear Iterative clustering on curvelet coefficients obtained at different decomposition levels of R, G, B components as well as directly on R, G, B components to get initial boundaries of superpixels. We obtain final boundaries of superpixels by taking the high probable boundaries from the initial formed boundaries. By incorporation of curvelets in five different scales the proposed method is capable of generating superpixels with high boundary adherence even in the presence of background change, object motion and noise. Superpixels generated by the proposed method are homogeneous in regions of complex texture and weak boundaries. The method has been tested on different images taken from Berkeley segmentation dataset and frames of several other videos. The algorithm under study is evaluated in terms of ten evaluation parameters: boundary recall, boundary precision, quality percentage, detection percentage, accuracy, Jaccard index, specificity, figure of merit, structural content, achievable segmentation accuracy. The results are tabulated, shown graphically and discussed in detail. The critical analysis of results show sound performance of the proposed method over other state-of- art 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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10 Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  2. Andrade F, Carrera EV (2015) Supervised evaluation of seed-based interactive image segmentation algorithms. Signal Processing, Images and Computer Vision (STSIVA), 2015 20th Symposium on: 1–7). IEEE

  3. Benesova W, Kottman M (2014) Fast superpixel segmentation using morphological processing. Proc Int Conf Machine Vision and Machine Learning (MVML)

  4. Candes EJ, Donoho DL (2000) Curvelets: a surprisingly effective nonadaptive representation for objects with edges. Stanford Univ Ca Dept of Statistics

  5. Chen J, Li Z, Huang B (2017) Linear spectral clustering superpixel. IEEE Trans Image Process 26(7):3317–3330

    Article  MathSciNet  MATH  Google Scholar 

  6. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  7. Fu P, Sun X, Sun Q (2017) Hyperspectral image segmentation via frequency-based similarity for mixed noise estimation. Remote Sens 9(12):1237

    Article  Google Scholar 

  8. Galasso F, Nagaraja NS, Cárdenas TJ, Brox T, Schiele B (2013) A unified video segmentation benchmark: annotation, metrics and analysis. Computer vision (ICCV), 2013 IEEE international conference on : 3527–3534. IEEE

  9. Giordano D, Murabito F, Palazzo S, Spampinato C (2015) Superpixel-based video object segmentation using perceptual organization and location prior. Proceedings of the IEEE conference on computer vision and pattern recognition: 4814–4822

  10. Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783

    Article  Google Scholar 

  11. Guo Y, Şengür A, Akbulut Y, Shipley A (2018) An effective color image segmentation approach using neutrosophic adaptive mean shift clustering. Measurement. 119:28–40

    Article  Google Scholar 

  12. Jampani V, Sun D, Liu MY, Yang MH, Kautz J:(2018) Superpixel sampling networks. European conference on computer vision: 363–380

  13. Khare M (2014) Binh, NT., Srivastava, RK., Khare, a.: ‘vehicle identification in traffic surveillance–complex wavelet transform based approach’. J Sci Technol 52(4A):29–38

    Google Scholar 

  14. Khare M, Srivastava RK, Khare A (2014) Single change detection-based moving object segmentation by using Daubechies complex wavelet transform. IET Image Process 8(6):334–344

    Article  Google Scholar 

  15. Khare M, Srivastava RK, Khare A (2017) Object tracking using combination of Daubechies complex wavelet transform and Zernike moment. Multimed Tools Appl 76(1):1247–1290

    Article  Google Scholar 

  16. Levinshtein A, Stere A, Kutulakos KN, Fleet DJ, Dickinson SJ, Siddiqi K (2009) Turbopixels: fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 31(12):2290–2297

    Article  Google Scholar 

  17. Liu C, Zhao Z (2013) Person re-identification by local feature based on super pixel. International conference on multimedia modeling: 196–205. Springer, Berlin, Heidelberg

  18. Liu MY, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. Computer vision and pattern recognition (CVPR), 2011 IEEE conference: 2097–2104. IEEE

  19. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. IJCAI: 1617–1623

  20. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing. 181:108–115

    Article  Google Scholar 

  21. Liu DY, Rao NN, Mei XM, Luo CS, Xing YW, Gan T (2017) An automatic annotation method for early esophageal cancers based on saliency guided superpixel segmentation. Proceedings of the International Conference on Bioinformatics and Computational Intelligence : 21–25

  22. Lu J, Dou F (2017) Bi-subspace saliency detection. Computing and communication workshop and conference (CCWC): 1–7

  23. Lv N, Chen C, Qiu T, Sangaiah AK (2018) Deep learning and Superpixel feature extraction based on contractive autoencoder for change detection in SAR images. IEEE Trans Indust Inform 14(12):5530–5538

    Article  Google Scholar 

  24. Machairas V, Faessel M, Cárdenas-Peña D, Chabardes T, Walter T, Decencière E (2015) Waterpixels. IEEE Trans Image Process 24(11):3707–3716

    Article  MathSciNet  MATH  Google Scholar 

  25. Mori G, Ren X, Efros AA, Malik J (2004 Jun 27) Recovering human body configurations: combining segmentation and recognition. Comput Vision Pattern Recogn 2004. CVPR 2004. Proc 2004 IEEE Comput Soc Conf 2:II IEEE

  26. Neubert P, Protzel P (2012) Superpixel benchmark and comparison. Proc Forum Bildverarbeitung 6:205–218

    Google Scholar 

  27. Neubert P, Protzel P (2014) Compact watershed and preemptive slic: on improving trade-offs of superpixel segmentation algorithms. Pattern recognition (ICPR), 2014 22nd international conference on: 996–1001. IEEE

  28. Nigam S, Khare A (2016) Integration of moment invariants and uniform local binary patterns for human activity recognition in video sequences. Multimed Tools Appl 75(24):17303–17332

    Article  Google Scholar 

  29. Peng Y, Lu BL (2017) Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing 261:242–252

    Article  Google Scholar 

  30. Poornima K, Kanchana R (2012) A method to align images using image segmentation. Int J Soft Comput Eng 2(1):294–298

    Google Scholar 

  31. Prakash O, Gwak J, Khare M, Khare A, Jeon M (2018) Human detection in complex real scenes based on combination of biorthogonal wavelet transform and Zernike moments. Optik-International J Light Electron Optics 157:1267–1281

    Article  Google Scholar 

  32. Rao SR, Mobahi H, Yang AY, Sastry SS, Ma Y (2009) Natural image segmentation with adaptive texture and boundary encoding. Asian conference on computer vision: 135–146. Springer, Berlin, Heidelberg

  33. Ren X, Malik J (2003) Learning a classification model for segmentation. Proc 9th Int Conf Comput Vision: 10–17. IEEE

  34. Ren CY, Prisacariu VA, Reid ID (2015) gSLICr: SLIC superpixels at over 250Hz. arXiv preprint arXiv:1509.04232

  35. Shen J, Du Y, Wang W, Li X (2014) Lazy random walks for superpixel segmentation. IEEE Trans Image Process 23(4):1451–1462

    Article  MathSciNet  MATH  Google Scholar 

  36. Shen J, Hao X, Liang Z, Liu Y, Wang W, Shao L (2016) Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans Image Process 25(12):5933–5942

    Article  MathSciNet  MATH  Google Scholar 

  37. Tang D, Fu H, Cao X (2012) Topology preserved regular superpixel. Multimedia and expo (ICME), 2012 IEEE international conference on: 765–768. IEEE

  38. Van den Bergh M, Boix X, Roig G, Van Gool L (2015) Seeds: Superpixels extracted via energy-driven sampling. Int J Comput Vis 111(3):298–314

    Article  MathSciNet  Google Scholar 

  39. Vedaldi A, Soatto S (2008) Quick shift and kernel methods for mode seeking. European conference on computer vision: 705–718. Springer, Berlin, Heidelberg

  40. Veksler O, Boykov Y, Mehrani P (2010) Superpixels and supervoxels in an energy optimization framework. European conference on computer vision: 211–224. Springer, Berlin, Heidelberg

  41. Welikala RA, Fraz MM, Foster PJ, Whincup PH, Rudnicka AR, Owen CG, Strachan DP, Barman SA (2016) Automated retinal image quality assessment on the UK biobank dataset for epidemiological studies. Comput Biol Med 71:67–76

    Article  Google Scholar 

  42. Yang D, Mao L, Ji M, Zhang R (2017) A superpixel segmentation algorithm with region correlation saliency analysis for video pedestrian detection. Control Conf (CCC) 26:5396–5399

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication, University of Allahabad, Allahabad, India

    Reddy Mounika Bommisetty & Ashish Khare

  2. Institute of Technology, Nirma University, Nirma, India

    Om Prakash

Authors
  1. Reddy Mounika Bommisetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Om Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashish Khare
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Khare.

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

Bommisetty, R.M., Prakash, O. & Khare, A. Video superpixels generation through integration of curvelet transform and simple linear iterative clustering. Multimed Tools Appl 78, 25185–25219 (2019). https://doi.org/10.1007/s11042-019-7554-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7554-z

Keywords

Search

Navigation