Automatic classification method for traditional moire patterns

A technology of automatic classification and moiré pattern, which is applied in the fields of instrument, calculation, character and pattern recognition, etc. It can solve the problems of inefficient and unsupervised manual classification, and achieve the effect of ensuring accuracy, high accuracy, and realizing automatic classification.

Active Publication Date: 2015-09-09
湖州优研知识产权服务有限公司
View PDF3 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The purpose of the present invention is to overcome the very inefficient shortcoming of artificial class

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Automatic classification method for traditional moire patterns
  • Automatic classification method for traditional moire patterns
  • Automatic classification method for traditional moire patterns

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] 1. Introduction to basic theory

[0042] 1. Multi-subclass central nearest neighbor propagation clustering algorithm

[0043] The MEAP algorithm is a clustering algorithm with a two-layer structure, such as figure 2 The algorithm shown assigns all data objects to the most suitable subclass center, and assigns each subclass center to the most suitable supercluster class center, so as to achieve the purpose of modeling the multi-subclass problem.

[0044] Similar to the AP algorithm, the MEAP algorithm establishes similarity information s(i,j) and connectivity information l(i,j) for each data object with other data objects. The algorithm sets bias parameters p=s(k,k) and pp=l(k,k) values ​​for each data object. The larger the value of p and pp, the corresponding data object is used as the candidate subclass center and super cluster The greater the possibility of the center, the more the number of clusters obtained, usually set p and pp as the median of the similarity m...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an automatic classification method for traditional moire patterns, mainly solves the problem of low manual classification efficiency of moire patterns, and realizes automatic classification of the moire patterns through moire pattern preprocessing, feature extraction and clustering processing. An implementation process includes the steps of: (1) preprocessing moire images, including three steps of unifying an image size, removing background noise and thinning moire image lines; (2) for shapes between moire images, main features of which are lines, adopting a shape context descriptor (SC) algorithm to extract features of the moire images, and obtaining initial similarity of the moire images through shape context distance; (3) optimizing a similarity matrix through an improved neighbor relation transfer algorithm; and (4) using the optimized similarity matrix as an input matrix of an MEAP algorithm to perform MEAP clustering processing, thereby realizing automatic classification. A clustering result shows that compared with SIFT-MEAP and ED-MEAP algorithms, the automatic classification method provided by the invention is higher in clustering accuracy, and a clustering effect is more ideal. At the same time, the automatic classification algorithm for the moire patterns proposed by the invention has very good reference significance for clustering analysis of other traditional art patterns.

Description

technical field [0001] The invention belongs to the technical field of cluster analysis and image classification, and relates to the preprocessing of moiré images, the extraction of shape context features, and the optimization of similarity matrices for neighbor relationship transfer. Specifically, it is an automatic classification method for clustering moiré images with the multi-subclass central neighbor propagation algorithm combined with neighbor relation transfer and shape context features. Background technique [0002] Among the traditional Chinese decorative patterns, moiré is a category with a long history, extremely rich shapes and unique oriental artistic charm. Moiré rheology is vivid, auspicious, and has various forms of expression, including changes of different monomers, and various grafted and continuous combination structures. It has been an important decorative element in various plane and three-dimensional shapes since ancient times. Today, it still has gr...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06K9/46G06K9/40
CPCG06V10/30G06V10/462G06F18/24137
Inventor 葛洪伟陈雷雷苏树智杨金龙
Owner 湖州优研知识产权服务有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap