One-Click-Based Perception for Interactive Image Segmentation

Existing deep learning-based interactive image segmentation methods have significantly reduced the user's interaction burden with simple click interactions. However, they still require excessive numbers of clicks to continuously correct the segmentation for satisfactory results. This article explores how to harvest accurate segmentation of interested targets while minimizing the user interaction cost. To achieve the above goal, we propose a one-click-based interactive segmentation approach in this work. For this particularly challenging problem in the interactive segmentation task, we build a top-down framework dividing the original problem into a one-click-based coarse localization followed by a fine segmentation. A two-stage interactive object localization network is first designed, which aims to completely enclose the target of interest based on the supervision of object integrity (OI). Click centrality (CC) is also utilized to overcome the overlapping problem between objects. This coarse localization helps to reduce the search space and increase the focus of the click at a higher resolution. A principled multilayer segmentation network is then designed by a progressive layer-by-layer structure, which aims to accurately perceive the target with extremely limited prior guidance. A diffusion module is also designed to enhance the information flow between layers. Besides, the proposed model can be naturally extended to multiobject segmentation task. Our method achieves the state-of-the-art performance under one-click interaction on several benchmarks.

Automated summary

Existing deep learning-based interactive image segmentation methods have reduced user interaction burden with simple clicks, but still require excessive clicks for satisfactory results. This article proposes a one-click-based interactive segmentation approach that achieves accurate segmentation of interested targets while minimizing user interaction cost. It introduces a top-down framework with a two-stage interactive object localization network for coarse localization and a progressive layer-by-layer segmentation network for fine segmentation. The proposed model achieves state-of-the-art performance on benchmarks for one-click interaction.