Adaptive fusion with multi-scale features for interactive image segmentation

Abstract

Multi-scale features are usually utilized to improve the performance of interactive image segmentation, however, they have varying leverages over the result of segmentation, for example, thinner segmentation results could be achieved by pixel-level features, but sensitive to image noise, and superpixel-level features could provide the semantic perception of the object, but easily lead to over-segmentations. Therefore, we propose an interactive image segmentation algorithm by adaptive fusion with multi-scale features (AFMSF). It intends on combining the multi-scale information adaptively for the segmentation via learning the influence coefficients of multi-scale features. First, multi-scale superpixel layers are generated by controlling the size of superpixels. Based on features of this multi-scale information, the similarity matrices and label priors with pixel-superpixel levels are then obtained. A fusion with diffusion strategy is designed to build the energy function by combining these multi-cues. Finally, the influence coefficient of each scale and the labeling are updated with each other until convergence. The algorithm we proposed is robust to diverse circumstances of objects, the experimental results on public interactive image segmentation datasets Graz, LHI, and MSRC validate the superior performance of the proposed method.