Robust interactive image segmentation with weak supervision for mobile touch screen devices

Abstract

In this paper, we present a robust and efficient approach for segmenting images with less and intuitive user interaction, particularly targeted for mobile touch screen devices. Our approach combines geodesic distance information with the flexibility of level set methods in energy minimization, leveraging the complementary strengths of each to promote accurate boundary placement and strong region connectivity while requiring less user interaction. To maximize the user-provided prior knowledge, we further propose a weakly supervised seed generation algorithm which enables image object segmentation without user-provided background seeds. Our approach provides a practical solution for visual object cutout on mobile touch screen devices, facilitating various media manipulation applications. We describe such a use case to selectively create oil painting effects on images. We demonstrate that our approach is less sensitive to seed placement and better at edge localization, whilst requiring less user interaction, compared with the state-of-the-art methods.