A Novel Approach for Website Aesthetic Evaluation based on Convolutional Neural Networks
Publish place: The Second International Conference on Web research
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
IRANWEB02_042
تاریخ نمایه سازی: 9 مرداد 1395
Abstract:
In this paper we propose a website aesthetic evaluation method. For achieving better performance, we have applied convolutional neural networks, which are one of the methods of deep learning research area. Using deep learning and convolutional neural networks for feature representation is one of the main tips that makes difference between our work and previous ones. Our system takes a screenshot of the website as input, and finally reports it is a good or bad website based on users’ country or not. For evaluation process, we represent the website screenshot using MemNet convolutional neural network. Then we decrease the extracted features dimension using principal component analysis algorithm. Finally, we classify them using a SVM classifier, which trained, based on users’ ratings. Furthermore, aesthetics evaluation in this research is language independent. It means the website’s language is not important and our method works for all languages.
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Authors
Masoud Ganj Khani
AmirKabir University of Technology
Mohammad Reza Mazinani
Malek-Ashtar University of Technology Tehran, Iran
Mohsen Fayyaz
Malek-Ashtar University of Technology Tehran, Iran
Mojtaba Hoseini
Malek Ashtar University of Technology Tehran, Iran