Perspective Based Model for Constructing Diverse Ensemble Members in Multi-classifier Systems for Multi-spectral Image Classification

This paper presents a perspective based model for creating diverse ensemble members in a multi-classifier system. With this technique different input feature sets are constructed using standard digital image processing and analyzing techniques viz. Haralick texture features, Gabor texture features, normalized difference vegetation index, standard deviation, spectral signatures, color spaces - CIELAB, HSV. These features are used as descriptors. Input feature sets are created as many as ensemble members. Input feature sets are discrete in nature because there is no common feature shared between any two input feature sets. Each one of these discrete input feature sets is utilized for training a particular ensemble member only. Each ensemble member would identify the classes independently and with completely different set of features. An empirical study for multi-spectral images shows that diverse and independent ensemble members can be constructed through our proposed technique. Results also show that proposed technique outperforms bagging in terms of individual member diversity and classification accuracy.