Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Journal of Shanghai Jiaotong University》 2005-04
Add to Favorite Get Latest Update

Non-negative Matrix Factorization Based Relevance Feedback Algorithm in Image Retrieval

LU Jin-jun,YANG Jie,LIANG Dong,CHANG Yu-chou (Inst. of Image Processing & Pattern Recognition, Shanghai Jiaotong Univ., Shanghai 200030, China)  
A novel relevance feedback algorithm was presented based on non-negative matrix factorization (NMF) learning in content-based image retrieval system. During the retrieval process, users can mark images similar to the query image as positive samples. Then the algorithm constructs an NMF basic matrix with the eigen vectors of the positive samples, which can be used to increase the accurate ratio for the image retrieval. Experiments were carried out on a big size database consisting of 500 images. The results show that accurate ratio of image retrieval can be increased much after using interactive NMF feedback algorithm.
【Fund】: 上海市科委项目"农业病虫害的远程监控和会诊系统研究"(03DZ19320)
【CateGory Index】: TP391.41
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved