Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.36MB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Textural Feature Extraction of Natural Objects for Image Classification
Vishal Krishna, Ayush Kumar, Kishore Bhamidipadi
Pages - 320 - 334     |    Revised - 30-11-2015     |    Published - 31-12-2015
Volume - 9   Issue - 6    |    Publication Date - November / December 2015  Table of Contents
MORE INFORMATION
KEYWORDS
Feature Extraction, Haralick, Classifiers, Cross-Validation.
ABSTRACT
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Nave Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 Timo Ojala, Matti Pietikainen and David Harwood, A comparative study of texture measures with classification based on feature distributions.
2 M. Pietikainen, T. Ojala, Z. Xu; Rotation-invariant texture classification using feature distributions
3 Eizan Miyamotol and Thomas Merryman Jr; FAST CALCULATION OF HARALICK TEXTURE FEATURES.
4 Frank, J. (1990) Quart. Rev. Biophys. 23, 281-329.
5 Baharak Goli and Geetha Govindan ; WEKA A powerful free software for implementing Bioinspired Algorithms;State Inter University Centre of Excellence in Bioinformatics, University of Kerala).
6 Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
7 Tom M. Mitchell; Machine Learning; McGraw Hill,2010.
8 David A. Freedsma; Statistical Models: Theory and Practice; Cambridge University Press, 2009, p. 128.
9 Shankar Pal, Shushmita Mitra; Multilayer Perceptron, Fuzzy Sets and Classification; IEEE Transactions on Neural Networks, Vol 3, September 1992.
10 Leo Breiman ; "Random Forests". Machine Learning 45 (1); 2001.
11 John Platt; Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines; 1998.
Mr. Vishal Krishna
Computer Science Georgia Institute of Technology Atlanta – 30332, US - India
vkrishna7@gatech.edu
Mr. Ayush Kumar
Computer science BITS Pilani, Goa Campus Goa – 403726, India - India
Associate Professor Kishore Bhamidipadi
Computer Science Engineering Manipal Institute of Technology Manipal – 576104, India - India