Advertisement

Object recognition using Hausdorff distance for multimedia applications

  • K. Senthil KumarEmail author
  • T. Manigandan
  • D. Chitra
  • L. Murali
Article
  • 22 Downloads

Abstract

The need for reliable and efficient systems for recognition of object from image is increasing day by day. A partial list of applications that may use such system includes searching and reading in hand written documents, recognizing digit on papers and others. In the existing work, Euclidean distance is used for recognizing object, but some of object it doesn’t work well. The major aim of the work is to introduce new object recognition. So the proposed work recognizing object using a shape context and Hausdorff distance is introduced. The process analyses the layout of the image into digits. In the first step, the shape context is computed for two point set and Hungarian algorithm is used to find the correspondence between two point set. The process evaluates the similarity of the two point set using Hausdorff distance. Finally, the error rate is calculated by considering the affine cost and shape context cost. The algorithm tested using the MNIST, COIL data sets and a private collection of hand written digits and encouraging results were obtained. The error rate is reduced to 0.72%.

Keywords

Object recognition Shape matching Hausdorff distance Digit recognition MNIST database 

Notes

References

  1. 1.
    Ali SS, UsmanGhani M (2014) Handwritten digit recognition using DCT and HMMs. 12th International Conference on Frontiers of Information Technology, vol. 1, p 303–306Google Scholar
  2. 2.
    Amit Y, Geman D, Wilder K (1997) Joint induction of shape features and tree classifiers. IEEE Trans Pattern Anal Mach Intell 19(11):1300–1305CrossRefGoogle Scholar
  3. 3.
    Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61(3):183CrossRefGoogle Scholar
  4. 4.
    Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490CrossRefGoogle Scholar
  5. 5.
    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4):509–522Google Scholar
  6. 6.
    Calabi E, Olver P, Shakiban C, Tannenbaum A, Haker S (1998) Differential and numerically invariant signature curves applied to object recognition. Int J Comput Vis 26:107–135Google Scholar
  7. 7.
    Coimbra MT, Cunha JS (2006) MPEG-7 visual descriptors—contributions for automated feature extraction in capsule endoscopy. IEEE Trans Circuits Syst Video Technol 16(5):628–637CrossRefGoogle Scholar
  8. 8.
    Forssén PE, Lowe DG (2007) Shape descriptors for maximally stable extreme regions. In 2007 IEEE 11th International Conference on Computer Vision, p 1–8Google Scholar
  9. 9.
    Forsyth D, Mundy J, Zisserman A, Brown C (1991) Projectively invariant representations using implicit algebraic curves. Image Vis Comput 9(2):130–136CrossRefGoogle Scholar
  10. 10.
    Gool LV, Moons T, Pauwels E, Oosterlinck A (1992) Semi-differential invariants. In: Mundy J, Zisserman A (eds) Geometric invariance in computer vision, p 193–214Google Scholar
  11. 11.
    Hong B-W, Soatto S (2014) Shape matching using multiscale integral invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(1):25–44Google Scholar
  12. 12.
    Hung WL, Yang MS (2004) Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recogn Lett 25(14):1603–1611CrossRefGoogle Scholar
  13. 13.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  14. 14.
    Li SZ (1999) Shape matching based on invariants. In: Progress in Neural Networks: Shape Recognition, vol. 6, p 203–228Google Scholar
  15. 15.
    McIlhagga W (2011) The canny edge detector revisited. Int J Comput Vis 91(3):251–261MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Nadernejad E, Sharifzadeh S, Hassanpour H (2008) Edge detection techniques: evaluations and comparisons. Appl Math Sci 2(31):1507–1520MathSciNetzbMATHGoogle Scholar
  17. 17.
    Prasad BG, Biswas KK, Gupta SK (2004) Region-based image retrieval using integrated color, shape, and location index. Comput Vis Image Underst 94(1–3):193–233CrossRefGoogle Scholar
  18. 18.
    Ranzato M, Huang FJ, Boureau Y, LeCun Y (2007) Unsupervised learning of invariant feature hierarchies with applications to object recognition. Proc. of Computer Vision and Pattern Recognition Conference (CVPR 2007)Google Scholar
  19. 19.
    Sapiro G, Tannenbaum A (1993) Affine invariant scale space. Int J Comput Vis 11(1):25–44CrossRefzbMATHGoogle Scholar
  20. 20.
    Zhao C, Shi W, Deng Y (2005) A new Hausdorff distance for image matching. Pattern Recogn Lett 26(5):581–586CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • K. Senthil Kumar
    • 1
    Email author
  • T. Manigandan
    • 2
  • D. Chitra
    • 2
  • L. Murali
    • 2
  1. 1.Tamilnadu College of EngineeringCoimbatoreIndia
  2. 2.P.A. College of Engineering and TechnologyPollachiIndia

Personalised recommendations