Learning multi-level representations for affective image recognition Article Swipe
Hao Zhang
,
Dan Xu
,
Gaifang Luo
,
Kangjian He
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s00521-022-07139-y
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s00521-022-07139-y
Images can convey intense affective experiences and affect people on an affective level. With the prevalence of online pictures and videos, evaluating emotions from visual content has attracted considerable attention. Affective image recognition aims to classify the emotions conveyed by digital images automatically. The existing studies using manual features or deep networks mainly focus on low-level visual features or high-level semantic representation without considering all factors. To better understand how deep networks are working for affective recognition tasks, we investigate the convolutional features by visualization them in this work. Our research shows that the hierarchical CNN model mainly relies on deep semantic information while ignoring the shallow visual details, which are essential to evoke emotions. To form a more general and discriminative representation, we propose a multi-level hybrid model that learns and integrates the deep semantics and shallow visual representations for sentiment classification. In addition, this study shows that class imbalance would affect performance as the main category of the affective dataset will overwhelm training and degenerate the deep networks. Therefore, a new loss function is introduced to optimize the deep affective model. Experimental results on several affective image recognition datasets show that our model outperforms various existing studies. The source code is publicly available.
Related Topics
Concepts
Computer science
Discriminative model
Convolutional neural network
Deep learning
Artificial intelligence
Semantics (computer science)
Visualization
Affective computing
Representation (politics)
Focus (optics)
Affect (linguistics)
Pattern recognition (psychology)
Machine learning
Natural language processing
Psychology
Politics
Programming language
Communication
Optics
Law
Political science
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00521-022-07139-y
- https://link.springer.com/content/pdf/10.1007/s00521-022-07139-y.pdf
- OA Status
- hybrid
- Cited By
- 17
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224288047
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4224288047Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00521-022-07139-yDigital Object Identifier
- Title
-
Learning multi-level representations for affective image recognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-22Full publication date if available
- Authors
-
Hao Zhang, Dan Xu, Gaifang Luo, Kangjian HeList of authors in order
- Landing page
-
https://doi.org/10.1007/s00521-022-07139-yPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00521-022-07139-y.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s00521-022-07139-y.pdfDirect OA link when available
- Concepts
-
Computer science, Discriminative model, Convolutional neural network, Deep learning, Artificial intelligence, Semantics (computer science), Visualization, Affective computing, Representation (politics), Focus (optics), Affect (linguistics), Pattern recognition (psychology), Machine learning, Natural language processing, Psychology, Politics, Programming language, Communication, Optics, Law, Political science, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 6, 2023: 5, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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