Natural Computing

, Volume 15, Issue 3, pp 359–370 | Cite as

Neighborhood granules and rough rule-base in tracking

  • Debarati Bhunia Chakraborty
  • Sankar K. Pal


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.


Neighborhood rough sets Granular computing Rough rule-base Video tracking 



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


  1. ChaLearn (2011) ChaLearn Gesture Dataset (CGD 2011). CaliforniaGoogle Scholar
  2. 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)Google Scholar
  3. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–577CrossRefGoogle Scholar
  4. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224–227CrossRefGoogle Scholar
  5. Du Y, Hu Q, Zhu P, Ma P (2011) Rule learning for classification based on neighborhood covering reduction. Inf Sci 181(24):5457–5467MathSciNetCrossRefGoogle Scholar
  6. Heikkila M, Pietikainen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28:657–662CrossRefGoogle Scholar
  7. Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594MathSciNetCrossRefMATHGoogle Scholar
  8. 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 VisionGoogle Scholar
  9. 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): 881–892Google Scholar
  10. 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 3–98Google Scholar
  11. Lai K, Bo L, Ren X, Fox D (2011) A large-scale hierarchical multi-view rgb-d object dataset. In: ICRA. (pp. 1817–1824)Google Scholar
  12. Maggio E, Cavallaro A (2010) Video tracking: theory and practice. Wiley, New YorkMATHGoogle Scholar
  13. Meher SK, Pal SK (2011) Rough-wavelet granular space and classification of multispectral remote sensing image. Appl Soft Comput 11(8):5662–5673CrossRefGoogle Scholar
  14. Mushrif MM, Ray AK (2008) Color image segmentation: rough-set theoretic approach. Pattern Recogn Lett 29(4):483–493CrossRefGoogle Scholar
  15. Pal SK, Ghosh A, Shankar BU (2000) Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. Int J Remote Sens 21:2269–2300CrossRefGoogle Scholar
  16. Pal SK, Shankar BU, Mitra P (2005) Granular computing, rough entropy and object extraction. Pattern Recogn Lett 26:2509–2517CrossRefGoogle Scholar
  17. Pal SK, Chakraborty D (2013) Unsupervised tracking, roughness and quantitative indices. Fundam Inform (IOS Press) 124(1–2):63–90MathSciNetGoogle Scholar
  18. Pawlak Z (1992) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Norwell, MAMATHGoogle Scholar
  19. Pedrycz W, Song M (2014) A granulation of linguistic information in ahp decision-making problems. Inf Fusion 17:93–101CrossRefGoogle Scholar
  20. Sen D, Pal SK (2009) Generalized rough sets, entropy, and image ambiguity measures. IEEE Trans Syst Man Cyberns Part B 39:117–128CrossRefGoogle Scholar
  21. 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):213–223Google Scholar
  22. Swirniaski RW (2001) Rough sets methods in feature reduction and classification. Int J Appl Math Comput Sci 11:565–582MathSciNetGoogle Scholar
  23. Wang Q, Chen F, Xu W, Yang MH (2012) Object tracking via partial least squares analysis. IEEE Trans Image Proc 21(10):4454–4465MathSciNetCrossRefGoogle Scholar
  24. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1264–1291CrossRefGoogle Scholar
  25. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

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