华东理工大学学报（自然科学版）2017，Vol.43Issue(1)：105-112,8.DOI:10.14135/j.cnki.1006-3080.2017.01.017
一种基于改进SURF和K-Means聚类的布料图像匹配算法
A Fabric Image Matching Algorithm Based on Improved SURF and K-Means Clustering
摘要
Abstract
Computer intelligent image processing technology can provide an effective aid for dress designer.By extracting the SURF features,the image shape of the cloth can be recognized.However,due to the high feature dimension and the grayscale based feature extraction method of SURF,there exist shortcomings,e.g,slow image matching speed and the matching result is not enough to match the characteristics of human visual.Hence,this paper proposes an adaptive SURF feature extraction algorithm based on wavelet transform and an image color analysis method based on K-Means clustering.By fusing the shape and color feature of the image,the matching speed is accelerated and the matching results are made more accord with the human visual perception.Experiments via 8 different kinds of fabric images show the effectiveness of the proposed algorithm.关键词
布料图像匹配/SURF特征/小波变换/K-Means聚类Key words
fabric image matching/SURF feature/wavelet transform/K-Means clustering分类
信息技术与安全科学引用本文复制引用
张雪芹,刘远远,曹逸尘,张鹏飞..一种基于改进SURF和K-Means聚类的布料图像匹配算法[J].华东理工大学学报（自然科学版）,2017,43(1):105-112,8.基金项目
国家自然科学基金(61371150) （61371150）
