A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs
المؤلفون المشاركون
Tong, Li
Zhang, Chi
Yu, Ziya
Wang, Linyuan
Yan, Bin
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-01
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Nowadays, visual encoding models use convolution neural networks (CNNs) with outstanding performance in computer vision to simulate the process of human information processing.
However, the prediction performances of encoding models will have differences based on different networks driven by different tasks.
Here, the impact of network tasks on encoding models is studied.
Using functional magnetic resonance imaging (fMRI) data, the features of natural visual stimulation are extracted using a segmentation network (FCN32s) and a classification network (VGG16) with different visual tasks but similar network structure.
Then, using three sets of features, i.e., segmentation, classification, and fused features, the regularized orthogonal matching pursuit (ROMP) method is used to establish the linear mapping from features to voxel responses.
The analysis results indicate that encoding models based on networks performing different tasks can effectively but differently predict stimulus-induced responses measured by fMRI.
The prediction accuracy of the encoding model based on VGG is found to be significantly better than that of the model based on FCN in most voxels but similar to that of fused features.
The comparative analysis demonstrates that the CNN performing the classification task is more similar to human visual processing than that performing the segmentation task.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yu, Ziya& Zhang, Chi& Wang, Linyuan& Tong, Li& Yan, Bin. 2020. A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1139475
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yu, Ziya…[et al.]. A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1139475
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yu, Ziya& Zhang, Chi& Wang, Linyuan& Tong, Li& Yan, Bin. A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1139475
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139475
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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