Sciweavers

Share
MIR
2005
ACM

Multiple random walk and its application in content-based image retrieval

11 years 7 months ago
Multiple random walk and its application in content-based image retrieval
In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by means of Markov random walks, one for images relevant to the query concept and the other for the irrelevant ones. The goal is to obtain the likelihood functions of both classes. Firstly, MRW generates two random walks with virtual absorbing boundaries, and uses the absorbing probabilities as the initial estimation of the likelihood functions. Then it refines the two random walks through an EMlike iterative procedure in order to get more accurate estimation of the likelihood functions. Class priors are also obtained in this procedure. Finally, MRW ranks all the unlabeled images in the database according to their posterior probabilities of being relevant. By using both labeled and unlabeled data, MRW can be seen as a transductive learning method, which has been demonstrated to outperform inductive ones by previous...
Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying M
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where MIR
Authors Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying Ma, Changshui Zhang
Comments (0)
books