CFP last date
20 January 2025
Reseach Article

A Sketch based Image Retrieval with Descriptor based on Constraints

by Dipika R. Birari, J. V. Shinde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 12
Year of Publication: 2016
Authors: Dipika R. Birari, J. V. Shinde
10.5120/ijca2016910923

Dipika R. Birari, J. V. Shinde . A Sketch based Image Retrieval with Descriptor based on Constraints. International Journal of Computer Applications. 146, 12 ( Jul 2016), 7-11. DOI=10.5120/ijca2016910923

@article{ 10.5120/ijca2016910923,
author = { Dipika R. Birari, J. V. Shinde },
title = { A Sketch based Image Retrieval with Descriptor based on Constraints },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 12 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number12/25448-2016910923/ },
doi = { 10.5120/ijca2016910923 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:13.783811+05:30
%A Dipika R. Birari
%A J. V. Shinde
%T A Sketch based Image Retrieval with Descriptor based on Constraints
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 12
%P 7-11
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To match sketch and real images some processing is required, because it is very difficult to directly match sketch and real image. Real images contain noise due to many reasons, which makes it very difficult for matching. For this descriptor is designed so that it can give best match by finding relationships between edges and line segments. By applying edge length as constraint, the retrieval performance is increases. Proposed framework tested on public datasets and results shows that proposed method improves SBIR performance significantly.

References
  1. Shu Wang, Jian Zhang,Ding, “Sketch-Based Image Retrieval Through Hypothesis-Driven Object Boundary Selectionwith HLR Descriptor. ieee transactions on multimedia, vol. 17, no. 7, july 2015.
  2. “Canny Edge Detection” March 23, 2009.
  3. M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa, “A descriptor for large scale image retrieval based on sketched feature lines,” in Proc. 6th Eurograph. Symp. Sketch-Based Interfaces Modeling, 2009, pp. 29–36.
  4. K. Bozas and E. Izquierdo, “Large scale sketch based image retrieval using patch hashing,” Adv. Visual Comput., vol. 7431, pp. 210–219.
  5. A. Chalechale, G. Naghdy, and A. Mertins, “Sketch-based image matching using angular partitioning,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 35, no. 1, pp. 28–41, Jan. 2005.
  6. J. M. Saavedra and B. Bustos, “An improved histogram of edge local orientations for sketch-based image retrieval,” in Proc. 32nd DAGM Conf. Pattern Recogn., 2010, pp. 432–441.
  7. R. Zhou, L. Chen, and L. Zhang, “Sketch-based image retrieval on a large scale database,” in Proc. 20th ACM Int. Conf. Multimedia, 2012, pp. 973–976.
  8. M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa, “Sketch-based image retrieval: Benchmark and bag-of-features descriptors,” IEEE Trans. Vis. Comput. Graph., vol. 17, no. 11, pp. 1624–1636, Nov.2011.
  9. R. Hu and J. Collomosse, “A performance evaluation of gradient field hog descriptor for sketch based image retrieval,” Comput. Vis. ImageUnderstand., vol. 117, no. 7, pp. 790–806, 2013.
  10. M. Eitz, J. Hays, and M. Alexa, “How do humans sketch objects,” ACM Trans. Graph., vol. 31, no. 4, pp. 44:1–44:10, Jul. 2012.
  11. C. Ma, X. Yang, C. Zhang, X. Ruan, M.-H. Yang, and O. Coporation, “Sketch retrieval via dense stroke features,” in Proc. Brit. Mach. Vis. Conf., 2013, vol. 2, pp. 65.1–65.11.
  12. R. Hu, T. Wang, and J. Collomosse, “A bag-of-regions approach to sketch-based image retrieval,” in Proc. IEEE Int. Conf. Image Process.,Sep. 2011, pp. 3661–3664.
  13. A. Shrivastava, T. Malisiewicz, A. Gupta, and A. A. Efros, “Data-driven visual similarity for cross-domain image matching,” ACM Trans. Graph., vol. 30, no. 6, pp. 154:1–154:10, Dec. 2011.
  14. T. Menp and M. Pietikinen, , J. Bigun and T. Gustavsson, Eds., “Multiscale binary patterns for texture analysis,” in Image Analysis, ser. Lecture Notes Comput. Sci.. Berlin, Germany: Springer-Verlag, 2003, vol. 2749, pp. 885–892.
  15. S. Salve and K. Jondhale, “Shape matching and object recognition using shape contexts,” in Proc. 3rd IEEE Int. Conf. Comput. Sci. Inf. Technol., 2010, vol. 9, pp. 471–474.
  16. SET. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: Internet image montage,” in Proc. ACM SIGGRAPH Asia, 2009, pp. 124:1–124:10.
  17. X. Cao, H. Zhang, S. Liu, X. Guo, and L. Lin, “Sym-fish: A symmetryaware flip invariant sketch histogram shape descriptor,” in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 313–320.
  18. O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman, “Total recall: Automatic query expansion with a generative feature model for object retrieval,” in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2007, pp. 1–8.
  19. Y.-L. Lin, C.-Y. Huang, H.-J. Wang, and W. Hsu, “3D sub-query expansion for improving sketch-based multi-view image retrieval,” in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 3495–3502.
  20. P. Sousa and M. J. Fonseca, “Sketch-based retrieval of drawings using spatial proximity,” J. Vis. Languages Comput., vol. 21, no. 2, pp. 69–80, 2010.
  21. T. Furuya and R. Ohbuchi, “Visual saliency weighting and cross-domain manifold ranking for sketch-based image retrieval,” in Proc. Int. Conf. Multimedia Modeling, 2014, pp. 37–49.
  22. G. Griffin, A. Holub, and P. Perona, “Caltech-256 object category dataset,” California Inst. Technol., Pasadena, CA, USA, Tech. Rep. CNS-TR-2007-001, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Descriptor sketch real images edge based histogram line relationship shaping edges.