JISE

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Journal of Information Science and Engineering, Vol. 33 No. 5, pp. 1177-1193

Image Classification Using Naive Bayes Classifier With Pairwise Local Observations

SHIH-CHUNG HSU¹, I-CHIEH CHEN¹ AND CHUNG-LIN HUANG^2,+
¹Department of Electrical Engineering
National Tsing-Hua University
Hsinchu, 300 Taiwan
²Department of M-Commerce and Multimedia Applications
Asia University
Taichung, 413 Taiwan
E-mail: clhuang@asia.edu.tw

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.

Keywords: local observation-based Naive Bayes classifier (NBPLO), salient region (SR), keypoint (KP), bag-of-feature (BoF), image clssification

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