An Efficient Technique for Color Image Classification Based On Lower Feature Content

Abstract

Image classification is backbone for image data available   around us. It is necessary to use  a technique for classified the data in a particular class. In multiclass classification used different Classifier technique  for the classification of data, such as binary classifier and support vector machine .In this paper we used an efficient classification technique as radial basis function.  A Radial Basis Function (RBF) neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain Gaussian transfer functions whose outputs are inversely proportional to the distance from the center of the neuron. For classification of data support vector machine (SVM) is used as binary classifier.  The some  approaches commonly used are the One-Against-One (1A1), One-Against-All (1AA),and SVM as Ant Colony Optimization(ACO). SVM-ACO decrease unclassified data and also decrease noise with outer line of data. Here SVM-RBF reduce noise with outer line data and complexity more than SVM-ACO.

Keywords-- Image classification; feature sampling; support vector machine; ACO; RBF.