Identification of Institutional Logo based on Wavelet Features
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10.5120/18826-0255 |
B v Dhandra, Shridevi Soma and Gururaj Mukarambi. Article: Identification of Institutional Logo based on Wavelet Features. International Journal of Computer Applications 107(15):9-13, December 2014. Full text available. BibTeX
@article{key:article,
author = {B.v. Dhandra and Shridevi Soma and Gururaj Mukarambi},
title = {Article: Identification of Institutional Logo based on Wavelet Features},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {107},
number = {15},
pages = {9-13},
month = {December},
note = {Full text available}
}
Abstract
The main aim of this paper is to recognize the logos of the input document so as to process the document for its classification and analysis, an algorithm is proposed using texture features based on Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) of object occurrence in a new tessellation of logo images and these features are given to the SVM and KNN classifier for recognizing the logos. The proposed algorithm is experimented on a data set of Institutional logos. The experimental results have shown the average recognition accuracy as 67. 74% using NN classifier, 79. 35% using KNN classifier and 87. 09% using SVM classifier. It is an initial attempt towards the classification of documents based on logos.
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