We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor

by Chinmoy Biswas, Joydeep Mukherjee
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 22
Year of Publication: 2015
Authors: Chinmoy Biswas, Joydeep Mukherjee
10.5120/20689-3574

Chinmoy Biswas, Joydeep Mukherjee . Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor. International Journal of Computer Applications. 117, 22 ( May 2015), 34-37. DOI=10.5120/20689-3574

@article{ 10.5120/20689-3574,
author = { Chinmoy Biswas, Joydeep Mukherjee },
title = { Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 22 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number22/20689-3574/ },
doi = { 10.5120/20689-3574 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:08.427121+05:30
%A Chinmoy Biswas
%A Joydeep Mukherjee
%T Logo Recognition Technique using Sift Descriptor, Surf Descriptor and Hog Descriptor
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 22
%P 34-37
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Logos sometimes also known as trademark have high importance in today's marketing world. Logo or trademark is of high importance because it carries the goodwill of the company and the product. Logo matching and recognition is important to discover either improper or unauthorized use of logos. Query images may come with different types of scale, rotation, affine distortion, illumination noise, highly occluded noise. Sift descriptor, surf descriptor and hog descriptor are very good features to use among the existing techniques to recognize the logo images from such difficulties more accurately.

References
  1. Sina Hassanzadeh, Hossein Pourghassem, "A Fast Logo Recognition Algorithm in Noisy Document Images, International Conference on Intelligent Computation and Bio-Medical Instrumentation,2011pp. 64-67.
  2. Aya Soffer and Hanan Samet, "Using Negative Shape Features for Logo Similarity Matching, "4th International Conference on Pattern Recognition, Brisbane, Qld. , vol: 1, pp. 571-573, 1998.
  3. Mikolajczyk, K. , Schmid, C. : A performance evaluation of local descriptors. In: CVPR. Volume 2. (2003) 257 – 263
  4. Jan Neuman, Hanan Samet, and Aya Soffer, "Integration of local and Global Shape Analysis for Logo classification", 4th International Workshop on Visual Form, Capri,Italy, pp. 769-778, 2001.
  5. Guangyu Zhu and David Doermann, "Automatic Document Logo Detection", 9th International Conference on Document Analysis and Recognition, Curitiba, Brazil, pp. 864-868, 2007.
  6. Liangfu Xia, Feihu Qi, Qianhao Zhou, "A Learning-based Logo Recognition Algorithm Using SIFT and Efficient Correspondence Matching", International Conference on Information and Automation, Changsha, Hunan, pp. 1767-1772, 2008.
  7. Wenju Li and Ling Li, "A Novel Approach for Vehicle-logo Location Based on Edge Detection and Morphological Filter", 2nd International Symposium on Electronic Commerce and Security, Nanchang, vol: 1, pp. 343-345, 2009.
  8. Nabeel Younus Khan,Brendan McCane,Geoff Wyvill SIFT and SURF Performance Evaluation Against Various Image Deformations on Benchmark Dataset, International Conference on Digital Image Computing: Techniques and Applications,2011 ,PP. 501-506.
  9. Syed Yasser Arafat, Syed Afaq Husain, Iftikhar Azim Niaz and Muhammad Saleem, "Logo Detection and Recognition in Video Stream", 5th International Conference on Digital Information Management, Thunder Bay, Canada, pp. 163 – 168, 2010.
  10. Liangfu Xia, Feihu Qi, Qianhao Zhou,"A Learning-based Logo Recognition Algorithm Using SIFT and Efficient Correspondence Matching ",IEEE International Conference on Information and Automation,2008,PP-1767-1773
  11. Dalal, N. and Triggs, B. , Histograms of oriented gradients for human detection, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 886893
  12. Stefan Romberg," From Local Features To Local Regions",PP. -841-845
  13. Sina Hassanzadeh and Hossein Pourghassem, "A Fast Logo Recognition Algorithm in Noisy Document Images",International Conference on Intelligent Computation and Bio-Medical Instrumentation, Wuhan, Hubei, pp. 64-67, 2011.
  14. Hossein Pourghassem,"A Hierarchical Logo Detection and Recognition Algorithm Using Twostage Segmentation and Multiple Classifiers", Fourth International Conference on Computational Intelligence and Communication Networks,2012,PP. 228-233.
  15. David G. Lowe, "Distinctive Image Features from Scale-Invariant KeyPoints", International Journal of Computer Vision, Netherlands, pp. 91-110, 2004.
  16. S. Yasser Arafat, Muhammad Saleem and S. Afaq Hussain, "Comparative Analysis of Invariant Schemes for Logo Classification", International Conference on Emerging Technologies, Islamabad, Pakistan, pp. 256-261, 2009.
  17. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in International Conference on Computer Vision & Pattern Recognition, 2005, pp. 886–893.
  18. O. Ludwig, D. Delgado, V. Goncalves, and U. Nunes, 'Trainable Classifier-Fusion Schemes: An Application To Pedestrian Detection,' In: 12th International IEEE Conference On Intelligent Transportation Systems, 2009, St. Louis, 2009. V. 1. P. 432-437.
  19. DATASET: Logo Database for Research http://lampsrv02. umiacs. umd. edu/projdb/project. php?id=47
  20. Paul Viola and Michael Jones. Rapid object detection using a boosted cascade of simple features. cvpr, 1:511, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

KeyPoint localization KeyPoint descriptor Interest Point Descriptor