کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6941808 | 870621 | 2016 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hierarchical aesthetic quality assessment using deep convolutional neural networks
ترجمه فارسی عنوان
ارزیابی کیفی زیبایی شناختی سلسله مراتبی با استفاده از شبکه های عصبی کانولوشن عمیق
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کلمات کلیدی
تجزیه و تحلیل تصویر زیبایی شناسی، شبکه های عصبی انعقادی، صحنه، هدف - شی، بافت،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Aesthetic image analysis has attracted much attention in recent years. However, assessing the aesthetic quality and assigning an aesthetic score are challenging problems. In this paper, we propose a novel framework for assessing the aesthetic quality of images. Firstly, we divide the images into three categories: “scene”, “object” and “texture”. Each category has an associated convolutional neural network (CNN) which learns the aesthetic features for the category in question. The object CNN is trained using the whole images and a salient region in each image. The texture CNN is trained using small regions in the original images. Furthermore, an A&C CNN is developed to simultaneously assess the aesthetic quality and identify the category for overall images. For each CNN, classification and regression models are developed separately to predict aesthetic class (high or low) and to assign an aesthetic score. Experimental results on a recently published large-scale dataset show that the proposed method can outperform the state-of-the-art methods for each category.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing: Image Communication - Volume 47, September 2016, Pages 500-510
Journal: Signal Processing: Image Communication - Volume 47, September 2016, Pages 500-510
نویسندگان
Yueying Kao, Kaiqi Huang, Steve Maybank,