Skip to main content

Advertisement

Springer Nature Link
Log in

Neighborhood granules and rough rule-base in tracking

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

This paper deals with several new methodologies and concepts in the area of rough set theoretic granular computing which are then applied in video tracking. A new concept of neighborhood granule formation over images is introduced here. These granules are of arbitrary shapes and sizes unlike other existing granulation techniques and hence more natural. The concept of rough-rule base is used for video tracking to deal with the uncertainties and incompleteness as well as to gain in computation time. A new neighborhood granular rough rule base is formulated which proves to be effective in reducing the indiscernibility of the rule-base. This new rule-base provides more accurate results in the task of tracking. Two indices to evaluate the performance of tracking are defined. These indices do not need ground truth information or any estimation technique like the other existing ones. All these features are demonstrated with suitable experimental results.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • ChaLearn (2011) ChaLearn Gesture Dataset (CGD 2011). California

  • Chuanga KS, Tzenga HL, Chena S, Wua J, Chenc TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1)

  • Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–577

    Article  Google Scholar 

  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224–227

    Article  Google Scholar 

  • Du Y, Hu Q, Zhu P, Ma P (2011) Rule learning for classification based on neighborhood covering reduction. Inf Sci 181(24):5457–5467

    Article  MathSciNet  Google Scholar 

  • Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28:657–662

    Article  Google Scholar 

  • Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594

    Article  MathSciNet  MATH  Google Scholar 

  • Janoch A, Karayev S, Jia Y, Barron JT, Fritz M, Saenko K, Darrell T (2011) A category-level 3-d object dataset: putting the kinect to work. In: ICCV Workshop on Consumer Depth Cameras for Computer Vision

  • Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu A (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7): 881–892

  • Komorouski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Pal SK, Skowron A (eds) Rough fuzzy hybridization: a new trend in decision-making. Springer, Singapore, pp 3–98

    Google Scholar 

  • Lai K, Bo L, Ren X, Fox D (2011) A large-scale hierarchical multi-view rgb-d object dataset. In: ICRA. (pp. 1817–1824)

  • Maggio E, Cavallaro A (2010) Video tracking: theory and practice. Wiley, New York

    MATH  Google Scholar 

  • Meher SK, Pal SK (2011) Rough-wavelet granular space and classification of multispectral remote sensing image. Appl Soft Comput 11(8):5662–5673

    Article  Google Scholar 

  • Mushrif MM, Ray AK (2008) Color image segmentation: rough-set theoretic approach. Pattern Recogn Lett 29(4):483–493

    Article  Google Scholar 

  • Pal SK, Ghosh A, Shankar BU (2000) Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. Int J Remote Sens 21:2269–2300

    Article  Google Scholar 

  • Pal SK, Shankar BU, Mitra P (2005) Granular computing, rough entropy and object extraction. Pattern Recogn Lett 26:2509–2517

    Article  Google Scholar 

  • Pal SK, Chakraborty D (2013) Unsupervised tracking, roughness and quantitative indices. Fundam Inform (IOS Press) 124(1–2):63–90

    MathSciNet  Google Scholar 

  • Pawlak Z (1992) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Norwell, MA

    MATH  Google Scholar 

  • Pedrycz W, Song M (2014) A granulation of linguistic information in ahp decision-making problems. Inf Fusion 17:93–101

    Article  Google Scholar 

  • Sen D, Pal SK (2009) Generalized rough sets, entropy, and image ambiguity measures. IEEE Trans Syst Man Cyberns Part B 39:117–128

    Article  Google Scholar 

  • Sinthanayothin C, Wongwaen N, Bholsithi W (2012) Skeleton tracking using kinect sensor and displaying in 3d virtual scene. Int J Adv Comput Technol (IJACT) 4(11):213–223

    Google Scholar 

  • Swirniaski RW (2001) Rough sets methods in feature reduction and classification. Int J Appl Math Comput Sci 11:565–582

    MathSciNet  Google Scholar 

  • Wang Q, Chen F, Xu W, Yang MH (2012) Object tracking via partial least squares analysis. IEEE Trans Image Proc 21(10):4454–4465

    Article  MathSciNet  Google Scholar 

  • Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1264–1291

    Article  Google Scholar 

  • Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

S. K. Pal acknowledges the J. C. Bose National Fellowship, Government of India and Indian National Academy of Engineering (INAE) Chair Professorship.

Author information

Authors and Affiliations

  1. Center for Soft Computing Research, Indian Statistical Institute, Kolkata, 700108, India

    Debarati Bhunia Chakraborty & Sankar K. Pal

Authors
  1. Debarati Bhunia Chakraborty
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sankar K. Pal
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Debarati Bhunia Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraborty, D.B., Pal, S.K. Neighborhood granules and rough rule-base in tracking. Nat Comput 15, 359–370 (2016). https://doi.org/10.1007/s11047-015-9493-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-015-9493-6

Keywords