Earticle

초록

영어

Visual sentiment analysis which aims to understand the emotion and sentiment in visual content has attracted more and more attention. In this paper, we propose a hybrid approach for visual sentiment concept classification with an unsupervised feature learning architecture called convolutional autoencoder. We first extract a representative set of unlabeled patches from the image dataset and discover useful features of these patches with sparse autoencoders. Then we use a convolutional neural network (CNN) to obtain feature activations on full images for sentiment concept classification. We also fine-tune the network with a progressive strategy in order to filter out noisy samples in the weakly labeled training data. Meanwhile, we use low-level visual features to classify visual sentiment concepts in a traditional manner. At last the classification results with unsupervised feature learning and that with traditional features are taken into account together with a fusion algorithm to make a final prediction. Extensive experiments on benchmark datasets reveal that the proposed approach can achieve better performance in visual sentiment analysis compared to its predecessors.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Overall Architecture
 4. Unsupervised Feature Learning and Progressive Fine-Tuning
  4.1. Sparse Autoencoder
  4.2. Feature Extraction
  4.3. Progressive Fine-Tuning
 5. Experiments
  5.1. ANP Detectors Training
  5.2. Annotation Accuracy
  5.3. Retrieval Performance
  5.4. Sentiment Prediction
 6. Conclusion
 References

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605