Advertisement

The Application of Sub-Pattern Approach in 2D Shape Recognition and Retrieval

  • Muzameel Ahmed
  • V. N. Manjunath Aradhya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

In 2D shape recognition and retrieval approach using shape feature extraction, statistical shape analysis methods such as PCA, ICA and NMF are commonplace, and these methods using subspace approach, have not been adequately investigated for recognition and retrieval of 2D shapes. The main hurdle in achieving higher recognition efficiency seems to be the shape sensitivity. In this paper we suggest, subspace method approach. The main idea is to use modular technique to improve the recognition and retrieval efficiency. Normally in the earlier methods proposed so far, a complete image is considered in training and matching process, in modular method approach partial image is used for training and matching the 2D images. The recognition and retrieval process is carried out in two phase, in the first phase uses the ridgelet transform applied. The second phase PCA is used for dimensionality reduction and to extract the effective features. For recognition and retrieval a study was conducted by using seventeen different distance measure technique. The training and testing process is conducted using leave-one-out strategy. The retrieval process is carried out by considering standard test “bull eyes” score. The proposed method is tested on the standard dataset MPEG-7. The experiment results of Subspace ridgelet PCA are compared with Subspace PCA method.

Keywords

2D object recognition Retrieval Subspace PCA Subspace ridgelet PCA Modular approach Principal component analysis Ridgelet transform Distance measure techniques 

References

  1. 1.
    Daliri, M.R., Torre, V.: Robust symbolic representation for shape recognition and retrieval. Pattern Recogn. 41, 1782–1798 (2008)Google Scholar
  2. 2.
    Bribiesca, E., Wilson, R.G.: A measure of 2d shape-of-object dissimilarity. Appl. Math. Lett. 10(6), 107–115 (1997)MATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Bandera, F.A., Urdiales, C., Sandoval, F.: 2d Object recognition based on curvature functions obtained from local histograms of the contour chain code. Pattern Recogn. Lett. 20, 49–55 (1999)MATHCrossRefGoogle Scholar
  4. 4.
    Rajeev, K., Peter, R.: Triplet geometric representation: a novel scale, translation and rotation invariant feature representation based on geometric constraints for recognition of 2d object features. Image Vis. Comput. 15, 235–249 (1997)CrossRefGoogle Scholar
  5. 5.
    Khalil, I.M., Bayoumi, M.M.: Invariant 2d object recognition using the wavelet modulus maxima. Patten Recog. Lett. 21, 863–872 (2000)Google Scholar
  6. 6.
    McNeill. G., Vijayakumar, S.: 2d shape classification and retrieval. In: Proceeding of International Joint Conference on Artificial Intelligence, pp. 1483–1488 (2005)Google Scholar
  7. 7.
    Malik, J., Belongie, S., Puzicha, J.: Shape matching and object recognition using shapes contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(24), 509–522 (2002)Google Scholar
  8. 8.
    Guru, D.S., Nagabhushan, P., Sheker, B.H.: (2d)2 fld: an efficient approach for appearance based object recognition. Neurocomputing, 69, 934–940 (2006)Google Scholar
  9. 9.
    Sun, T.-H., Liu, C.-S., Tien, F.-C.: Invariant 2d object recognition using eigenvalues of covariance matrices, re-sampling and autocorrelation. 35, 1966–1977 (2008)Google Scholar
  10. 10.
    Hwang, E., Nam, Y., Kim, D.: A similarity-based leaf image retrieval scheme: Joining shape and venation features. Comput. Vision Image Underst. 110, 245–259 (2008)Google Scholar
  11. 11.
    Tomasz, A.: Invariant object recognition using radon-based transform. Comput. Inform. 24, 183–199 (2005)MATHMathSciNetGoogle Scholar
  12. 12.
    Manuele, B., Pietro, L.: 2d shape recognition using biological sequencing alignment tools. Pattern Recogn. 1359–1362 (2012)Google Scholar
  13. 13.
    Miroslaw, M: Radon transformation and principal component analysis method applied in postal address recognition task. Int. J. Comput. Sci. Appl. 7(3), 33–44 (2010)Google Scholar
  14. 14.
    Candes, E.J., Donoho, D.L.: Ridgelet: a key to higher dimensional intermittency. Philoso. Trans. R. Soc. 2495–2509 (1999)Google Scholar
  15. 15.
    Moschetti, F., Grania, L., Vandergheynst, P.: Ridgelet transform applied to motion compensated images.: In Proceedings of the ICASSP, pp. 381–384 (2003)Google Scholar
  16. 16.
    Do, M.N., Vetterli, M.: Finite ridgelet transform for image representation. IEEE Trans. Image process. (2002)Google Scholar
  17. 17.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  18. 18.
    Perlibakar, Vytautas: Distance measure for pca-based face recognition. Pattern Recogn. Lett. 25, 711–724 (2004)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJain UniversityBengaluruIndia
  2. 2.Department of MCASri Jayachamarajarendra College of EngineeringMysoreIndia

Personalised recommendations