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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
Feature Extraction, Haralick, Classifiers, Cross-Validation.
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.
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Mr. Vishal Krishna
Computer Science Georgia Institute of Technology Atlanta – 30332, US - India
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