Towards Interactive Image Segmentation by Dynamic and Iterative Spanning Forest

Abstract

Interactive image segmentation aims to partition the image into background and foreground objects by taking into account seeds inserted by users. Nowadays, many methods are capable of generating segmentations with few user interactions, especially region-based techniques. However, such methods are highly sensitive to seed displacement and quantity, and delineation errors are often propagated to the final segmentation result. Recently, a novel superpixel segmentation framework, named Dynamic and Iterative Spanning Forest (DISF), was proposed, which achieved top delineation performance while assessing many seed-based state-of-the-art methods’ drawbacks. In this work, we propose interactive DISF (iDISF), an interactive segmentation framework, by modifying each step of DISF to consider user-validated information. DISF uses the Image Foresting Transform (IFT) framework for computing an optimum-path forest rooted in a seed set in the delineation step. To consider path and image gradient variation, we propose three new connectivity functions for the IFT. Finally, we also propose two new seed removal strategies for detecting relevant seeds for subsequent iterations. Results show segmentation improvements for minimal user effort—i.e., a single click—and show theoretical advances that may benefit recent optimum-path-based interactive methods from scribbles.