Image Classification Using Naive Bayes Classifier With Pairwise Local Observations

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題名:	Image Classification Using Naive Bayes Classifier With Pairwise Local Observations
作者:	徐士中;SHIH-CHUNG HSU;陳羿捷;I-CHIEH CHEN;黃仲陵;Chung-Lin Huang
貢獻者:	行動商務與多媒體應用學系
日期:	2017-09
上傳時間:	2017-11-27 03:48:03 (UTC+0)
摘要:	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.
關聯:	JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
顯示於類別:	[行動商務與多媒體應用學系] 期刊論文

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