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Scaring or pleasing: exploit emotional impact of an image

Published: 29 October 2012 Publication History

Abstract

Automatic image emotion analysis has emerged as a hot topic due to its potential application on high-level image understanding. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local regions, we propose a novel affective image classification system based on bilayer sparse representation (BSR). The BSR model contains two layers: The global sparse representation (GSR) is to define global similarities between a test image and all the training images; and the local sparse representation (LSR) is to define similarities of local regions' appearances and their co-occurrence between a test image and all the training images. The experiments on real data sets demonstrate that our system is effective on image emotion recognition.

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Published In

cover image ACM Conferences
MM '12: Proceedings of the 20th ACM international conference on Multimedia
October 2012
1584 pages
ISBN:9781450310895
DOI:10.1145/2393347

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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Author Tags

  1. affective image classification
  2. bilayer sparse representation

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MM '12
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MM '12: ACM Multimedia Conference
October 29 - November 2, 2012
Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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  • (2023)PerceptSent - Exploring Subjectivity in a Novel Dataset for Visual Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322523814:3(1817-1831)Online publication date: 1-Jul-2023
  • (2022)Visual Sentiment Analysis With Social Relations-Guided Multiattention NetworksIEEE Transactions on Cybernetics10.1109/TCYB.2020.302776652:6(4472-4484)Online publication date: Jun-2022
  • (2022)Gabor CNN Based Intelligent System for Visual Sentiment Analysis of Social Media Data on Cloud EnvironmentIEEE Access10.1109/ACCESS.2022.322826310(132455-132471)Online publication date: 2022
  • (2022)Deep Convolutional Neural Networks with Transfer Learning for Visual Sentiment AnalysisNeural Processing Letters10.1007/s11063-022-11082-355:4(5087-5120)Online publication date: 18-Nov-2022
  • (2021)TGSL-Dependent Feature Selection for Boosting the Visual Sentiment ClassificationSymmetry10.3390/sym1308146413:8(1464)Online publication date: 10-Aug-2021
  • (2021)Data measurement and aerobics online teaching based on SAR target detection networkPersonal and Ubiquitous Computing10.1007/s00779-021-01641-1Online publication date: 16-Aug-2021
  • (2020)OutdoorSentACM Transactions on Information Systems10.1145/338518638:3(1-28)Online publication date: 21-Apr-2020
  • (2020)Visual Sentiment Analysis With Active LearningIEEE Access10.1109/ACCESS.2020.30249488(185899-185908)Online publication date: 2020
  • (2020)A deep learning architecture of RA-DLNet for visual sentiment analysisMultimedia Systems10.1007/s00530-020-00656-726:4(431-451)Online publication date: 25-May-2020
  • (2019)Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph LearningIEEE Transactions on Multimedia10.1109/TMM.2018.286771821:4(1062-1075)Online publication date: Apr-2019
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