Academic Journal

Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs

التفاصيل البيبلوغرافية
العنوان: Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs
المؤلفون: Gando, Gota, Yamada, Taiga, Sato, Haruhiko, Oyama, Satoshi, Kurihara, Masahito
بيانات النشر: Elsevier
المجموعة: Hokkaido University Collection of Scholarly and Academic Papers (HUSCAP) / 北海道大学学術成果コレクション
مصطلحات موضوعية: Aggregation systems, Machine learning, Deep learning, Illustrations
Time: 548
الوصف: Systems for aggregating illustrations require a function for automatically distinguishing illustrations from photographs as they crawl the network to collect images. A previous attempt to implement this functionality by designing basic features that were deemed useful for classification achieved an accuracy of only about 58%. On the other hand, deep neural networks had been successful in computer vision tasks, and convolutional neural networks (CNNs) had performed good at extracting such useful image features automatically. We evaluated alternative methods to implement this classification functionality with focus on deep neural networks. As the result of experiments, the method that fine-tuned deep convolutional neural network (DCNN) acquired 96.8% accuracy, outperforming the other models including the custom CNN models that were trained from scratch. We conclude that DCNN with fine-tuning is the best method for implementing a function for automatically distinguishing illustrations from photographs.
نوع الوثيقة: article in journal/newspaper
اللغة: English
Relation: http://hdl.handle.net/2115/72243; Expert Systems with Applications, 66: 295-301; http://dx.doi.org/10.1016/j.eswa.2016.08.057
DOI: 10.1016/j.eswa.2016.08.057
الاتاحة: http://hdl.handle.net/2115/72243
https://doi.org/10.1016/j.eswa.2016.08.057
Rights: © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ ; http://creativecommons.org/licenses/by-nc-nd/4.0/
رقم الانضمام: edsbas.A28D43B4
قاعدة البيانات: BASE
الوصف
DOI:10.1016/j.eswa.2016.08.057