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Affective image classification via semi-supervised learning from web images

Affective image classification via semi-supervised learning from web images Affective image classification has drawn increasing research attentions in the affective computing and multimedia communities. Despite many solutions proposed in the literature, it remains a major challenge to bridge the semantic gap between visual features of images and their affective characteristics, partly due to the lack of adequate training samples, which can be largely ascribed to the all-consuming nature of affective image annotation. In this paper, we propose a novel affective image classification algorithm based on semi-supervised learning from web images (SSL-WI). This algorithm consists of four major steps, including color and texture feature extraction, baseline classifier construction, feature selection, and jointly using training images and retrieved web images to re-train the classifier. We have applied this algorithm, the baseline classifier that is not trained by web images, and two state-of-the-art algorithms to differentiating color images in a three-dimensional discrete emotional space. Our results suggest that, with the scheme of semi-supervised learning from web images, the proposed algorithm is able to produce more accurate affective image classification than other three approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Affective image classification via semi-supervised learning from web images

Li, Na; Xia, Yong
Multimedia Tools and Applications , Volume 77 (23) – May 31, 2018
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18 pages

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
DOI
10.1007/s11042-018-6131-1
Publisher site
See Article on Publisher Site

Abstract

Affective image classification has drawn increasing research attentions in the affective computing and multimedia communities. Despite many solutions proposed in the literature, it remains a major challenge to bridge the semantic gap between visual features of images and their affective characteristics, partly due to the lack of adequate training samples, which can be largely ascribed to the all-consuming nature of affective image annotation. In this paper, we propose a novel affective image classification algorithm based on semi-supervised learning from web images (SSL-WI). This algorithm consists of four major steps, including color and texture feature extraction, baseline classifier construction, feature selection, and jointly using training images and retrieved web images to re-train the classifier. We have applied this algorithm, the baseline classifier that is not trained by web images, and two state-of-the-art algorithms to differentiating color images in a three-dimensional discrete emotional space. Our results suggest that, with the scheme of semi-supervised learning from web images, the proposed algorithm is able to produce more accurate affective image classification than other three approaches.

Journal

Multimedia Tools and Applications – Springer Journals

Published: May 31, 2018

References

  • Do prospect-based emotions enhancebelievability of game characters? A casestudy in the context of a dice game
    Bosse, T; Zwanenburg, E
  • Multichannel texture analysis using localized spatial filters
    Bovik, AC; Clark, M; Geisler, WS
  • Assistive image comment robot—a novel mid-level concept-based representation
    Chen, YY; Chen, T; Liu, T; Liao, HYM; Chang, SF
  • Semantics in visual information retrieval
    Colombo, C; Del Bimbo, A; Pala, P
  • Compact object descriptors from local colour invariant histograms
    Geusebroek, JM
  • Annotation propagation in image databases using similarity graphs
    Houle, ME; Oria, V; Satoh, SI; Sun, J
  • The art of color (Chinese version)
    Itten, J
  • Design and form-the basic course at the Bauhous (Chinese version)
    Itten, J
  • Fuzzy similarity-based emotional classification of color images
    Joonwhoan, L; EunJong, P
  • Aesthetics and emotions in images
    Joshi, D; Datta, R; Fedorovskaya, E; Luong, QT
  • Objective evaluation of color design II
    Kawamto, N; Soen, T
  • Towards making unlabeled data never hurt
    Li, YF; Zhou, ZH
  • A study of colour emotion and colour preference. Part I: colour emotions for single colours
    Ou, LC; Luo, MR; Woodcock, A; Wright, A
  • Consensus analysis and modeling of visual aesthetic perception
    Park, TS; Zhang, BT
  • Objective evaluation of color design
    Soen, T; Shimada, T; Akita, M
  • Color emotions for multi-colored images
    Solli, M; Lenz, R
  • Emotional image and musical information retrieval with interactive genetic algorithm
    Sung-Bae, C
  • Image annotation by k nn-sparse graph-based label propagation over noisily tagged web images
    Tang, J; Hong, R; Yan, S; Chua, T-S; Qi, G-J; Jain, R
  • Semantic-gap-oriented active learning for multilabel image annotation
    Tang, JH; Zha, ZJ; Tao, D; Chua, TS
  • Affective labeling in a content-based recommender system for images
    Tkalcic, M; Odic, A; Kosir, A; Tasic, J
  • Affective Computing and Intelligent Interaction
    Wang, Shangfei; Wang, Xufa
  • Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images
    Wang, Q; Meng, Z; Li, X
  • Deep metric learning for crowdedness regression
    Wang, Q; Wan, J; Yuan, Y
  • Locality constraint distance metric learning for traffic congestion detection
    Wang, Q; Wan, J; Yuan, Y
  • Guest editorial: emotion in games
    Yannakakis, GN; Isbister, K; Paiva, A; Karpouzis, K
  • Semi-supervised image classification based on local and global regression
    Zhao, M; Zhan, C; Wu, Z; Tang, P
  • Hierarchical support vector machine based structural classification with fused hierarchies
    Zhao, S; Han, Y; Zou, Q; Hu, Q
  • Multi-view semi-supervised learning for image classification
    Zhu, S; Sun, X; Jin, D