Image Classification Using Naive Bayes Classifier With Pairwise Local Observations

被引：3

作者：

Hsu, Shih-Chung ^{[1
]}

Chen, I-Chieh ^{[1
]}

Huang, Chung-Lin ^{[2
]}

机构：

[1] Natl Tsing Hua Univ, Dept Elect Engn, Hsinchu 300, Taiwan

[2] Asia Univ, Dept M Commerce & Multimedia Applicat, Taichung 413, Taiwan

来源：

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING | 2017年 / 33卷 / 05期

关键词：

local observation-based Naive Bayes classifier (NBPLO); salient region (SR); keypoint (KP); bag-of-feature (BoF); image clssification; K-SVD; SCALE; RECOGNITION; FEATURES;

D O I：

10.6688/JISE.2017.33.5.5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：1177 / 1193

页数：17

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