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A Supervised Classifier for Natural Images

Published on October 2013 by A. Kalaivani, M. Deepika, S. Janarathanan
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National Conference on Recent Trends in Computer Applications
Foundation of Computer Science USA
NCRTCA - Number 1
October 2013
Authors: A. Kalaivani, M. Deepika, S. Janarathanan
b117c478-d266-4614-9bfa-5dff47c2d28a

A. Kalaivani, M. Deepika, S. Janarathanan . A Supervised Classifier for Natural Images. National Conference on Recent Trends in Computer Applications. NCRTCA, 1 (October 2013), 22-27.

@article{
author = { A. Kalaivani, M. Deepika, S. Janarathanan },
title = { A Supervised Classifier for Natural Images },
journal = { National Conference on Recent Trends in Computer Applications },
issue_date = { October 2013 },
volume = { NCRTCA },
number = { 1 },
month = { October },
year = { 2013 },
issn = 0975-8887,
pages = { 22-27 },
numpages = 6,
url = { /proceedings/ncrtca/number1/13636-1307/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Trends in Computer Applications
%A A. Kalaivani
%A M. Deepika
%A S. Janarathanan
%T A Supervised Classifier for Natural Images
%J National Conference on Recent Trends in Computer Applications
%@ 0975-8887
%V NCRTCA
%N 1
%P 22-27
%D 2013
%I International Journal of Computer Applications
Abstract

Image Classification is used to organize images so that they fall into different thematic classes. Image classification leads to easy retrieval of data based on the text query by the user. The main idea behind image segmentation is to make the images easier to recognize dominating objects from the background. Images are classified based on the low level features. In this paper, an efficient supervised classifier is identified for classifying natural images.

References
  1. Nilima Kulkarni: Color Thresholding Method for Image Segmentation of Natural Images , International Journal of Image, Graphics and Signal Processing, vol. 1, 2012, 28-34.
  2. Retno Kusumaningrum, Aniati Murni Arymurthy, "Color and Texture Feature for Remote Sensing – Image Retreival System: A Comparitive Study,"IJCSI International Journal of Computer Science Issues, Vol. 8,Issue 5, No 2, September 2011.
  3. I. Felci Rajam, S. Valli, "Semantic Region Based Image Retreival by Extracting the dominant Region and Semantic Learning," Journal of Computer Science 7(3),2011.
  4. K. Naresh Babu, Sake. Pothalaiah, Dr. K. Ashok Babu, "Image Retreival Color, Shape and Texture Features using Conteny Based," International Journal of Engineering Science and Technology,Vol. 2(9), 2010.
  5. Patrick Deotsch, Christian Buck, Pavlo Golik, Niklas Hoppe, Michael Kramp, Johannes Laudenberg, Christian Oberdorfer, Pascal Steingrube, Jens Forster, Arne Mauser,"Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge," JMLR, Workshop and Coference Proceedings, 2009.
  6. Mohd Fauzi Bin Othman, Thomas Moh Shan Yau, "Comparison of Different Classification Techniquesusing WEKA for Breast Cancer," IFMBE Proceedings, Vol. 15, 2007.
  7. RuggeriF. , Faltin F. , Kenett R. ,"Bayesian Networks," Encyclopedia of Statistics in Quality and Reliability, Wiley&Sons, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Block Segmentation Feature Extraction Supervised Classification .