Deep Learning-Based 3D Shape Feature Extraction on Flash Animation Style

Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multim...

Full description

Saved in:
Bibliographic Details
Published in:Wireless communications and mobile computing 2022-03, Vol.2022, p.1-9
Main Authors: Zhu, Mengru, Lee, Jong Han
Format: Article
Language:English
Subjects:
Algorithms
Animation
Classification
Communication
Deep learning
Digital broadcasting
Digital media
Distance learning
Feature extraction
Humanities
Internet
Machine learning
Multimedia
Neural networks
Personal computers
Science education
Search engines
Software
Teaching
Virtual reality
Web site design
Websites
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multimedia interaction and presentation capabilities, is widely used in distance education, high-quality course websites, Q&A platforms, etc. With the continuous development of deep learning, the 3D shape feature extraction method combined with deep learning has become a hot research topic. In this paper, we combine deep learning with traditional 3D shape feature extraction methods, so that we can not only break the bottleneck of nondeep learning methods but also improve the accuracy of 3D shape data classification and retrieval tasks, especially in the case of non-rigid 3D shapes. The scheme in this paper not only does not require a large number of training samples but also its feature extraction for flash animation is accurate. Experiments show that the success rate of accurate feature extraction of this paper’s scheme is higher than that of the state-of-the-art methods.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/7999312