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Reseach Article

Novel Method for 3D Objects Retrieval

by Dayanand Jamkhandikar, Surendra Pal Singh, V.D. Mytri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 14
Year of Publication: 2015
Authors: Dayanand Jamkhandikar, Surendra Pal Singh, V.D. Mytri
10.5120/ijca2015906689

Dayanand Jamkhandikar, Surendra Pal Singh, V.D. Mytri . Novel Method for 3D Objects Retrieval. International Journal of Computer Applications. 128, 14 ( October 2015), 1-6. DOI=10.5120/ijca2015906689

@article{ 10.5120/ijca2015906689,
author = { Dayanand Jamkhandikar, Surendra Pal Singh, V.D. Mytri },
title = { Novel Method for 3D Objects Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 14 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number14/22938-2015906689/ },
doi = { 10.5120/ijca2015906689 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:36.012758+05:30
%A Dayanand Jamkhandikar
%A Surendra Pal Singh
%A V.D. Mytri
%T Novel Method for 3D Objects Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 14
%P 1-6
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Access to appropriate information is a fundamental necessity in the modern society. Recent years have seen a rapid growth in use of digital images. To retrieve similar images from a database Content Based Image Retrieval (CBIR) system is used. Shape is most widely used feature in CBIR system. Among various approaches of shape retrieval, edge based shape descriptors are the most commonly been used. These representations are processed via different edge estimation tools and algorithms. However in the process of edge based coding, the discontinuous edge regions result in discarding of image regions or the inclusion of such regions increases the number of processing regions. A selection of non-informative region will lead to less descriptive feature, which leads to increase in system overhead and lowering of retrieval accuracy. Contours are observed to be the best representative approach for shape descriptor. To improve the retrieval accuracy of a 3D image retrieval system, a contour based coding is developed. In this approach along with the shape feature depth feature is extracted from the contor. The proposed 3D contor Shape depth (3D CSD) approach uses less number of feature vectors for representation. Simulation observations show that 3D CSD improves the retrieval accuracy and reduce the computation time.

References
  1. Novotni, M. and Klein, R., “3D Zernike Descriptors for Content Based Shape Retrieval”, Proceedings of the 8th ACM Symposium on Solid Modelling and Applications, Seattle, Washington, USA, 2003, pp. 216- 225.
  2. Bustos, B. Keim, D., Saupe, D. and Schreck,T., “Content-based 3D Object Retrieval”, In IEEE Transactions on Computer Graphics and Applications, Vol. 27, No. 4, 2007, pp. 22-27.
  3. Mademlis, A., Darasb, P., Tzovarasb, D., and Strintzis, M.G., “3D Object Retrieval Using the 3D Shape Impact Descriptor”, Journal of Pattern Recognition, Vol. 42 No.11, 2009, pp. 2447-2459 .
  4. Cao, L., Liu, J., and Tang, X.,“3D Object Retrieval Using 2D Line Drawing and Graph Based Relevance Feedback”, Proceedings of the 14th Annual ACM International Conference on Multimedia, Santa Barbara, CA, USA, 2006, pp. 105 – 108.
  5. Ichida, H., Itoh, Y., Kitamura, Y., and Kishino, F., “Interactive Retrieval of 3D Shape Models Using Physical Objects”, Proceedings of the 12th Annual ACM International Conference on Multimedia, New York, NY, USA, 2004, pp. 692 – 699.
  6. Gong, B., Xu, C., Liu, J. and Tang, X., “Boosting 3D Object Retrieval by Object Flexibility”, Proceedings of the 7th ACM International Conference on Multimedia, Beijing, China, 2009, pp. 525-528.
  7. B. Bustos, D. Keim, D. Saupe, Tobias Schreck, Content-Based 3D Object Retrieval, IEEE Computer graphics and Applications, 27(4): 22- 27, 2007.
  8. Vajramushti, N., Kakadiaris, I.A., Theoharis, T., and Papaioannou, G., “Efficient 3D Object Retrieval Using Depth Images”, Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, New York, NY, USA, 2004, pp. 189 – 196.
  9. Canny J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986; PAMI-8(6):679-698.
  10. Gonzalez R, Woods R, Eddins S. Digital Image processing using MATLAB. Upper Saddle River, N.J.: McGraw Hill Education; 2010.
  11. Dayanand Jamkhandikar, Dr. Surendra pal Singh, Dr. V.D. Mytri. Empirical Coding for Curvature Based Linear Representation in Image Retrieval System. IOSR Journal of Computer Engineering (IOSR-JCE).2015; 17(3): 5-16.
  12. Tsai, P., Shah, M.Shape from shading using linear-approximation. IVC 12.1994: 487–498
  13. S. A. Nene, S. K. Nayar, H. Murase. Columbia Object Image Library (COIL-100). Technical Report CUCS-006-96. February 1996.
  14. Moon H, Chellappa R, Rosenfeld A. Optimal edge-based shape detection. IEEE Transactions on Image Processing. 2002; 11(11):1209-1227.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR 3D-CSD shape feature contor edge.