red dotted line. Next, we ignore the unknown samples (note that we still have the extra negative samples shown as green minus symbols) and gradually update the learned classifier to classify the labeled positive sample and the extra negative samples. As the unknown positive samples (shown as blue plus symbols) are not taken as negative samples, they no longer push the decision boundary. As a result, the decision boundary (as shown as black dotted line) will be moved a bit further from the known positive sample and classify a few more unknown positive samples as positive samples.

If the propagation is carefully controlled, we can treat the newly classified positive samples as known labeled data for the next stage, as shown in Fig. \[fig:intuition\](d). We repeat the process above to iteratively learn the boundary and propagate the label set. Each iteration here is called a stage, and we use $L^{s}=\{\mathbf{x}_i|i=1,\ldots M_s\}$ to denote the label set in the $s$-th stage, where $M_s$ is the number of expanded bounding boxes in one image. Finally, we combine all the expanded positive samples and take all the others as background to learn the final decision boundary.

![Framework of the proposed label propagation by 