Selective Sampling Based on Dynamic Certainty Propagation for Image Retrieval

Abstract

In relevance feedback of image retrieval, selective sampling is often used to alleviate the burden of labeling by selecting only the most informative data to label. Traditional data selection scheme often selects a batch of data at a time and label them all together, which neglects the data’s correlation and thus jeopardizes the effectiveness. In this paper, we propose a novel Dynamic Certainty Propagation (DCP) scheme for informative data selection. For each unlabeled data, we define the notion of certainty to quantify our confidence in its predicted label. Every time, we only label one single data point with the lowest degree of certainty. Then we update the rest unlabeled data’s certainty dynamically according to their correlation. This one-by-one labeling offers us extra guidance from the last labeled data for the next labeling. Experiments show that the DCP scheme outperforms the traditional method evidently.