Image Classification using SVM-RBF in the field of Image Processing
Jitendra Kumar · IJIRMPS - International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences · 2013
Multi- class classification plays an important role in image classification. In this paper a feature sampling technique of image classification is to be proposed. For the process of optimization we used radial basis function algorithm for the proper selection of feature sub set selection. A function is radial basis (RBF) if its output depends on the distance of the input from a given stored vector. In a RBF network one hidden layer uses neurons with RBF activation functions describing local receptors. Then one output node is used to combine linearly the outputs of the hidden neurons. Different possibilities include: Modify the design of the SVM, as in order to incorporate the multi-class learning directly in the quadratic solving algorithm. Combine several binary classifiers: One-against- One (OAO) applies pair wise comparisons between classes, while One-against-All (OAA) compares a given class with all the others put together. OAO and OAA classification based on SVM technique is efficient process, but this SVM based feature selection generate result on the unclassified of data. When the scale of data set increases the complexity of pre-processing is also increases, it is difficult to reduce noise and outlier of data set.