Mixture of deep CNN-based ensemble model for image retrieval

Hsin Kai Huang, Chien Fang Chiu, Chien Hao Kuo, Yu Chi Wu, Narisa N.Y. Chu, Pao Chi Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509023332
DOIs
StatePublished - 27 Dec 2016
Event5th IEEE Global Conference on Consumer Electronics, GCCE 2016 - Kyoto, Japan
Duration: 11 Oct 201614 Oct 2016

Publication series

Name2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016

Conference

Conference5th IEEE Global Conference on Consumer Electronics, GCCE 2016
Country/TerritoryJapan
CityKyoto
Period11/10/1614/10/16

Keywords

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

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