Content-Based Image Retrieval for Painting Style with Convolutional Neural Network

Authors

  • Wei Sheng Tan
  • Wan Yoke Chin Tunku Abdul Rahman University College (TARUC)
  • Khai Yin Lim

DOI:

https://doi.org/10.54552/v82i3.122

Keywords:

Retrieval System, Deep Learning, Convolutional Neural Network, Artwork Classification

Abstract

With the availability of large paintings dataset on the online platform in recent years, it opens up a new research perspective and researcher started to investigate paintings in a new and different ways. While Convolutional Neural Networks (CNN) have been successfully applied in computer vision domains, several researchers started to investigate the possibility of utilizing CNN for artwork classification and retrieval system. Image retrieval has been one of the most difficult disciplines in digital image processing because it requires scanning a large database for images that are comparable to the query image. It is commonly known that retrieval performance is largely influenced by feature representations in trained algorithm and similarity measures. Deep Learning has recently advanced significantly, and deep features based on deep learning have been widely used because it has been demonstrated that the features have great generalisation. In this paper, a convolutional neural network (CNN) is utilised to classify painting’s style and extract deep and high-level features from the paintings. Next, were used as an image retrieval system to retrieve similar images in artwork style of content which is useful for search or recommendation systems in online art collection. Our experiments show that this strategy significantly improves the performance of content-based image retrieval for the style retrieval task of painting and beat the current state-of-art for painting style classification.

Downloads

Published

24-11-2022

How to Cite

Tan, W. S., Chin, W. Y., & Lim, K. Y. (2022). Content-Based Image Retrieval for Painting Style with Convolutional Neural Network. The Journal of The Institution of Engineers Malaysia, 82(3). https://doi.org/10.54552/v82i3.122