Active Learning for Interactive Segmentation with Expected Confidence Change

Using human prior information to perform interactive segmentation plays a significant role in figure/ground segmentation. In this paper, we propose an active learning based approach to smartly guide the user to interact on crucial regions and can quickly achieve accurate segmentation results. To select the crucial regions from unlabeled candidates, we propose a new criterion, i.e. selecting the ones which maximize the expected confidence change (

ECC

) over all unlabeled regions. Given an image represented by oversegmented regions, our active learning based approach iterates following three steps: 1) selecting crucial unlabeled regions with maximal

ECC

; 2) refining the selected regions; 3) updating appearance models based on the refined regions and performing image segmentation. Specifically, a constrained random walks algorithm is employed for segmentation, since it can efficiently produce confidence for computing

ECC

during active learning. Compared to the conventional interactive segmentation methods, the experimental results demonstrate our method can largely reduce the interaction efforts while maintaining high figure/ground segmentation accuracy.