Image classification using pairwise local observations based Naive Bayes classifier

We present an image classification method which consists of salient region (SR) detection, local feature extraction, and pairwise local observations based Naive Bayes classifier (NBPLO). Different from previous image classification algorithms, we propose a scale, translation, and rotation invariant image classification algorithm. Based on the discriminative pairwise local observations, we develop the structure object model based Naive Bayes classifier for image classification. We do the experiments using Scene-15 and Caltech-101 database and compare the experiment results of bag-of-features (BoF) and SPM algorithms.