Representative sampling with certainty propagation for image retrieval
Jian Cheng, Biao Niu, Yikai Fang, Hanqing Lu · 2011
Selective sampling has been widely used in relevance feedback of image retrieval to alleviate the burden of labeling by selecting the most informative instances for user to label. Traditional sample selection scheme often selects a batch of instances each time and label them simultaneously, which ignores the correlation among instances and results in redundant labeling. In this paper, we propose an improved representative sampling method with certainty propagation to improve the performance of sampling. In our method, two kinds of correlations among instances are explored to reduce the redundancy in sampling. One is the correlation between labeled instances and unlabeled instances. The other is the correlation among unlabeled instances. Extensive experiments show that the proposed method achieve encouraging results.