CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Effect of Salient Features in Object Recognition

by Kashif Ahmad, Nasir Ahmad, Kamal Haider, Muhammad Jawad Ikram
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 13
Year of Publication: 2013
Authors: Kashif Ahmad, Nasir Ahmad, Kamal Haider, Muhammad Jawad Ikram
10.5120/9991-4839

Kashif Ahmad, Nasir Ahmad, Kamal Haider, Muhammad Jawad Ikram . Effect of Salient Features in Object Recognition. International Journal of Computer Applications. 61, 13 ( January 2013), 34-39. DOI=10.5120/9991-4839

@article{ 10.5120/9991-4839,
author = { Kashif Ahmad, Nasir Ahmad, Kamal Haider, Muhammad Jawad Ikram },
title = { Effect of Salient Features in Object Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 13 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number13/9991-4839/ },
doi = { 10.5120/9991-4839 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:09:03.205357+05:30
%A Kashif Ahmad
%A Nasir Ahmad
%A Kamal Haider
%A Muhammad Jawad Ikram
%T Effect of Salient Features in Object Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 13
%P 34-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the SIFT and SURF based recognition, the paper presents the impact of salient features in object recognition. We use the two well-known image descriptors in the bag of words framework on five online available standard datasets. Experiments show that by introducing saliency in the bag of words model, state-of-the-art performance can still be retained while reducing considerable amount of data processing and thus achieving faster execution times.

References
  1. C. Schmid and R. Mohr. "Local greyvalue invariants for image retrieval. " TPAMI, 1997.
  2. D. Lowe. ," Object recognition from local scale-invariant features",ICCV1999.
  3. Herbert Bay , Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robuts Features", ECCV. 2006 .
  4. Csurka, G. , Dance, C. , Fan, L. , Williamowski, J. , Bray, C. : Visual categorization with bags of keypoints. In: ECCV'04 workshop on Statistical Learning in Computer Vision. (2004) 59–74
  5. Lowe, D. "Distinctive image features from scale-invariant keypoints. " IJCV 60 (2004)
  6. Fergus, R. , Fei-Fei, L. , Perona, P. , Zisserman, A. : Learning object categories from google's image search. In: ICCV. (2005) II: 1816–1823
  7. Leung, T. , Malik, J. "Representing and recognizing the visual appearance of materials using three-dimensional textons. " IJCV 43 (2001) 29–44
  8. A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, S. Belongie, "Objects in context", ICCV, 2007.
  9. A. Torralba, "Contextual priming for object detection", International Journal of Computer Vision (IJCV) 53 (2) (2003) 153–167
  10. J. Verbeek, B. Triggs, Scene segmentation with CRFs learned from partially labeled images, in: NIPS, vol. 11, 2008.
  11. L. Wolf, S. Bileschi, "A critical view of context", International Journal of Computer Vision (2006).
  12. P. Duygulu, K. Barnard, J. F. G. de Freitas and D. A. Forsyth, "Object recognition as Machine translation: Learning a Lexicon for a fixed image vocabulary" Computer Vision ECCV2002.
  13. Hall, D. , Leibe, B. , Schiele, B. : "Saliency of interest points under scale changes". In: BMVC. (2002)
  14. Tie Liu, "Learning to detect a salient object" Pattern Analysis and Machine Intelligence, IEEE Transactions on Feb. 2011
  15. Timor Kadir and Michael Brady "Saliency, scale and image description" Jurnal of computer vision (2001)
  16. P. J. Flynn. "Saliencies and symmetries: Toward 3d object recognition from large model databases". In CVPR'92, pages 322–327, 1992
  17. B. Schiele and J. L. Crowley. "Probabilistic object recognition using multidimensional receptive ?eld histograms". In ICPR96, Vienna, Austria, 1996.
  18. N. Sebe and M. S. Lew. "Salient points for content-based retrieval. " In BMVC'01, pages 401–410, 2001.
  19. K. N. Walker, T. F. Cootes, and Chris Taylor. "Locating salient object features". In BMVC'98,pages 557–566, 1998.
  20. Serre, T. , Kouh, M. , Cadieu, C. , Knoblich, U. , Kreiman, G. , Poggio, T. , "A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex", MIT-CSAIL-TR-2005-082.
  21. Beis, J. , and Lowe, D. G "Shape indexing using approximate nearest-neighbour search in high-dimensional spaces", Conference on Computer Vision and Pattern Recognition, Puerto Rico, 1997, pp. 1000–1006.
  22. K. Mikolajczyk and C. Schmid. "A Performance Evaluation of Local Descriptors". In Interna-tional Conference on Computer Vision and Pattern Recognition, volume 2, pages 257–263 jun 2003
  23. P. A. Viola and M. J. Jones. "Rapid object detection using a boosted cascade of simple features". In CVPR (1), pages 511 –518, 2001
  24. Luo Juan, Oubong Gwun, "A Comparison of SIFT, PCA-SIFT and SURF"
  25. Itti, L. , Koch, C. , & Niebur, E. "A model of saliency-based visual attention for rapid scene analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1254–1259. 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Salient features Saliency Object recognition SIFT SURF Interest point detectors Feature points

Powered by PhDFocusTM