Earticle

초록

영어

In modern society, visual content like images and videos is increasingly becoming a new form of media to express users’ opinions on the Internet. As a complement to textual sentiment analysis, visual sentiment analysis intends to provide more robust information for data analytics by extracting emotion and sentiment toward topics and events from images and videos. Inspired by recent works that applied deep convolutional neural networks (CNN) to this challenging problem, we proposed a framework for image sentiment analysis with a novel deep neural network called Network in Network (NIN) which intends to improve the discriminability for local patches within receptive fields. We trained our network on a dataset consisting of nearly half a million Flickr images and minimized the effect of noisy training data by fine-tuning the network in a progressive manner. Extensive experiments conducted on manually labeled Twitter images show that the proposed architecture performs better in visual sentiment analysis than conventional CNN and other traditional algorithms.

목차

Abstract
 1. Introduction
 2. Network in Network
 3. Overall Structure and Progressive Fine-Tuning
 4. Experiments
  4.1. Training on Flickr Dataset
  4.2. Twitter Test Dataset
  4.3. Transfer Learning
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Zuhe Li [ School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China, School of Computer and Communication Engineering,Zhengzhou University of Light Industry, Zhengzhou 450002, China ]
• Yangyu Fan [ School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China ]
• Fengqin Wang [ School of Computer and Communication Engineering,Zhengzhou University of Light Industry, Zhengzhou 450002, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605