2008
@article{QZL2008,
vgclass = {refpap},
author = {Tao Qin and Xu-Dong Zhang and Tie-Yan Liu and De-Sheng
Wang and Wei-Ying Mai and Hong-Jiang Zhang},
title = {An active feedback framework for image retrieval},
journal = {Pattern Recognition Letters},
volume = {29},
number = {5},
pages = {637--646},
month = {April},
year = {2008},
url = {http://dx.doi.org/10.1016/j.patrec.2007.11.015},
abstract = {In recent years, relevance feedback has been studied
extensively as a way to improve performance of content-based image
retrieval (CBIR). Since users are usually unwilling to provide much
feedback, the insufficiency of training samples limits the success of
relevance feedback. In this paper, we propose two strategies to tackle
this problem: (i) to make relevance feedback more informative by
presenting representative images for users to label; (ii) to make use
of unlabeled data in the training process. As a result, an active
feedback framework is proposed, consisting of two components,
representative image selection and label propagation. For practical
implementation of this framework, we develop two coupled algorithms
corresponding to the two components, namely, overlapped subspace
clustering and multi-subspace label propagation. Experimental results
on a very large-scale image collection demonstrated the high
effectiveness of the proposed active feedback framework.},
}