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    Title: Image Classification Using Naive Bayes Classifier With Pairwise Local Observations
    Authors: 徐士中;SHIH-CHUNG HSU;陳羿捷;I-CHIEH CHEN;黃仲陵;Chung-Lin Huang
    Contributors: 行動商務與多媒體應用學系
    Date: 2017-09
    Issue Date: 2017-11-27 11:48:03 (UTC+8)
    Abstract: 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.
    Appears in Collections:[Department of Applied Informatics and Multimedia] Journal Article

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