  Low level images representation in feature space performs poorly for
classification with high accuracy since this level of representation is not
able to project images into the discriminative feature space. In this work, we
propose an efficient image representation model for classification. First we
apply Hidden Markov Model (HMM) on ordered grids represented by different type
of image descriptors in order to include causality of local properties existing
in image for feature extraction and then we train up a separate classifier for
each of these features sets. Finally we ensemble these classifiers efficiently
in a way that they can cancel out each other errors for obtaining higher
accuracy. This method is evaluated on 15 natural scene dataset. Experimental
results show the superiority of the proposed method in comparison to some
current existing methods
