Paper Info
Title
Combining long-term learning and active learning with semi-supervised method for content-based image retrieval
Abstract
To improve the efficiency of relevance feedback in image retrieval, an integrated method of long-term learning and active learning is proposed. In early stage, more positive samples are obtained through long-term learning. The problem of biased training samples is effectively solved through a semi-supervised method that uses not only labeled training samples but also unlabeled ones; therefore an accurate initial SVM classifier is obtained. In later stage, through active learning algorithm that selects the most useful samples in database to solicit the user for labeling, samples required for labeling by users decreased largely and convergence rate increased greatly. Experimental results on 5000 Corel images library have shown that the proposed method can greatly improve both the efficiency and the performance, and it can accelerate the convergence to user's query concept as well
Year
DOI
Venue
2006
10.1109/MMMC.2006.1651327
MMM
Keywords
Field
DocType
learning (artificial intelligence),semi-supervised learning,svm classifier,active learning,content-based image retrieval,image retrieval,biased training samples,relevance feedback,long-term learning,semisupervised method,content-based retrieval,support vector machines,semi supervised learning,learning artificial intelligence
Stability (learning theory),Relevance feedback,Active learning,Semi-supervised learning,Pattern recognition,Active learning (machine learning),Computer science,Support vector machine,Image retrieval,Artificial intelligence,Machine learning,Content-based image retrieval
Conference
ISBN
Citations 
PageRank 
1-4244-0028-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yihua Zhou175.33
Yuanda Cao2104.30
Le-peng Bi300.34
Ben-jie Wei420.72