Mixture of Deep CNN-based Fnsemble Model for Image Retrieval

被引：0

作者：

Huang, Hsin-Kai ^{[1
]}

Chiu, Chien-Fang ^{[1
]}

Kuo, Chien-Hao ^{[1
]}

Wu, Yu-Chi ^{[1
]}

Chu, Narisa N. Y. ^{[2
]}

Chang, Pao-Chi ^{[1
]}

机构：

[1] Natl Cent Univ, Dept Commun Engn, Zhungli, Taiwan

[2] CWLab Int, Thousand Oaks, CA USA

来源：

2016 IEEE 5TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS | 2016年

关键词：

Content-based image retrieval; Ensemble learning; Deep learning; Neural networks; Convolutional neural networks;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper proposes an aggregate (or mixture) of ensemble models for image retrieval based on deep Convolutional Neural Networks (CNN). It utilizes two kinds of deep learning networks, AlexNet and Network In Network (NIN), to obtain image features, and to compute weighted average feature vectors for image retrieval. Based on experimental results, the aggregate ensemble architecture effectively enhances learning with higher accuracy than single CNN in image classification. When the proposed aggregate of deep CNN-based ensemble model is applied to CIFAR-10 and CIFAR-100 datasets, it is shown to achieve 0.867 and 0.526 mean average precision in image retrieval, respectively.

引用

页数：2

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