Image Classification Using Naive Bayes Classifier With Pairwise Local Observations

We propose a pairwise local observation-based Naive Bayes (NBPLO) classifier for image classification. First, we find the salient regions (SRs) and the Keypoints (KPs) as the local observations. Second, we describe the discriminative pairwise local observations using Bag-of-features (BoF) histogram. Third, we train the object class models by using random forest to develop the NBPLO classifier for image classification. The two major contributions in this paper are multiple pairwise local observations and regression object class model training for NBPLO classifier. In the experiments, we test our method using Scene-15 and Caltech-101 database and compare the results with the other methods.