Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification Article Swipe
Er-Yang Huan
,
Guihua Wen
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1155/2019/1258782
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1155/2019/1258782
Constitution classification is the basis and core content of TCM constitution research. In order to improve the accuracy of constitution classification, this paper proposes a multilevel and multiscale features aggregation method within the convolutional neural network, which consists of four steps. First, it uses the pretrained VGG16 as the basic network and then refines the network structure through supervised feature learning so as to capture local image features. Second, it extracts the image features of different layers from the fine-tuned VGG16 model, which are then dimensionally reduced by principal component analysis (PCA). Third, it uses another pretrained NASNetMobile network for supervised feature learning, where the previous layer features of the global average pooling layer are outputted. Similarly, these features are dimensionally reduced by PCA and then are fused with the features of different layers in VGG16 after the PCA. Finally, all features are aggregated with the fully connected layers of the fine-tuned VGG16, and then the constitution classification is performed. The conducted experiments show that using the multilevel and multiscale feature aggregation is very effective in the constitution classification, and the accuracy on the test dataset reaches 69.61%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2019/1258782
- https://downloads.hindawi.com/journals/cmmm/2019/1258782.pdf
- OA Status
- hybrid
- Cited By
- 15
- References
- 19
- Related Works
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- OpenAlex ID
- https://openalex.org/W2995025705
All OpenAlex metadata
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https://openalex.org/W2995025705Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2019/1258782Digital Object Identifier
- Title
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Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-12-20Full publication date if available
- Authors
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Er-Yang Huan, Guihua WenList of authors in order
- Landing page
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https://doi.org/10.1155/2019/1258782Publisher landing page
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https://downloads.hindawi.com/journals/cmmm/2019/1258782.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/cmmm/2019/1258782.pdfDirect OA link when available
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Artificial intelligence, Pattern recognition (psychology), Pooling, Feature (linguistics), Principal component analysis, Convolutional neural network, Computer science, Feature extraction, Focus (optics), Physics, Linguistics, Optics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 2, 2022: 2, 2021: 3Per-year citation counts (last 5 years)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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