CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Automated Color Logo Recognition Technique using Color and Hog Features

by Upasana Maity, Joydeep Mukherjee
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 2
Year of Publication: 2017
Authors: Upasana Maity, Joydeep Mukherjee
10.5120/ijca2017914715

Upasana Maity, Joydeep Mukherjee . Automated Color Logo Recognition Technique using Color and Hog Features. International Journal of Computer Applications. 170, 2 ( Jul 2017), 38-41. DOI=10.5120/ijca2017914715

@article{ 10.5120/ijca2017914715,
author = { Upasana Maity, Joydeep Mukherjee },
title = { Automated Color Logo Recognition Technique using Color and Hog Features },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 2 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number2/28046-2017914715/ },
doi = { 10.5120/ijca2017914715 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:27.412508+05:30
%A Upasana Maity
%A Joydeep Mukherjee
%T Automated Color Logo Recognition Technique using Color and Hog Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 2
%P 38-41
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research work purposes an automated system for the identification of the color logo images. Color logo images are recognized using a color feature namely Color Moments and an Histogram Oriented Gradients feature. Color is modeled using Mean and Standard Deviation. Firstly we extract color moments feature from an image, and then we consider histogram analysis and make a summation of each color color bin. Classification is done using Support Vector Machine Classifier (SVM). Experimental verification is done using a dataset of 500 images divided into 10 classes.

References
  1. 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.
  2. David Doermann, Ehud Rivlin, and Isaac Weis, “Logo Recognition using geometric Invariants, ”2nd International Conference on Document Analysis and Recognition, Tsukuba Science City, pp. 894-897, 1993.
  3. 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.
  4. 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.
  5. N. Senthilkumaran and R. Rajesh,” Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches”, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
  6. Souvik Ghosh and Ranjan Parekh “Automated Color Logo Recognition System based on Shape and Color Features” International Journal of Computer Applications (0975 – 8887) Volume 118– No.12, May 2015
  7. Andre Folkers and Haran Samet, “Content-Base Image Retrieval using Fourier Descriptors on a Logo Database,” 16th International Conference on Pattern Recognition, Quebec, Canada, vol: 3, pp. 521-524, 2002.
  8. 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.
  9. 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.
  10. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Logo recognition color features histogram features SVM classifier.

Powered by PhDFocusTM