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Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification
المؤلفون المشاركون
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-20
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
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%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huan, Er-Yang& Wen, Gui-Hua. 2019. Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1130453
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huan, Er-Yang& Wen, Gui-Hua. Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1130453
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huan, Er-Yang& Wen, Gui-Hua. Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1130453
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130453
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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