Filling the gaps: Reducing the complexity of networks for multi-attribute image aesthetic prediction

Magzhan Kairanbay, John See, Lai-Kuan Wong, Yong-Lian Hii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computational aesthetics have seen much progress in recent years with the increasing popularity of deep learning methods. In this paper, we present two approaches that leverage on the benefits of using Global Average Pooling (GAP) to reduce the complexity of deep convolutional neural networks. The first model fine-tunes a standard CNN with a newly introduced GAP layer. The second approach extracts global and local CNN codes by reducing the dimensionality of convolution layers with individual GAP operations. We also extend these approaches to a multi-attribute network which uses a style network to regularize the aesthetic network. Experiments demonstrate the capability of attaining comparable accuracy results while reducing training complexity substantially.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages3051-3055
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 22 Feb 2018
Event24th IEEE International Conference on Image Processing 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Conference

Conference24th IEEE International Conference on Image Processing 2017
Abbreviated titleICIP 2017
CountryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Aesthetics
  • Convolutional Neural Network
  • Global Average Pooling
  • Multi-attribute Network
  • Style

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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