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 HUANG2,+
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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