SUPPORT VECTOR MACHINES


Introduction of support vector machines for image classification paradigm.
We have so far learnt about the various classification algorithms such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Decision Trees and Independent Component Analysis (ICA).They however have restricted performance when the decision surfaces required are non-linear. So, when data sets are inherently separated by a non-linear decision surface, it is advantageous to use a non-linear classifier. Neural Networks and K-NN classifier are some of the widely used non-linear classifiers.

In this talk, we introduce a new classification scheme called the Support Vector Machine (SVM) which has gained prominence in the past couple of years. It has some interesting features, like control over generalizability, maximum expected error and most importantly has a discriminative rather than a representative character. We present preliminary results of using SVMs to classify the images in set 01 in the USFS database into HSBE, MSBE and LSBE.