Analysis of the regional styles of artistic patterns based on convolutional neural network utilizing spatial attention mechanism


Abstract

Image recognition has received significant attention in recent years and is bringing about unprecedented changes in the art field. With the rapid development of deep learning and computer vision technology, artificial intelligence can effectively analyze and identify different art styles, thereby providing strong support for multiple areas such as art preservation, creation, and research. Traditional pattern regional style identification relies on the accumulated experience of researchers to establish discrimination standards and determine the regional style of patterns. Due to the complexity and diversity of pattern regional styles, manual identification is usually inefficient and prone to errors. To address these issues, we propose a new method that combines the spatial attention mechanism and a convolutional neural network (CNN) to create a model for identifying the regional styles of patterns, achieving rapid and accurate recognition results. We utilized resources from the Metropolitan Museum of Art, the Smithsonian Institution, and the National Palace Museum (Taipei) to build a self-made dataset, classifying patterns by continent and training the model for recognition. The model has a short recognition time (4.63x10-4 seconds per image) and a high accuracy rate (87%). The above experimental results demonstrate that this method has excellent performance in pattern regional style recognition.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.

  • PeerJ Computer Science does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at [email protected].