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 tech- niques. 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.

From scribbles inside and outside the desired object, the Interactive DISF performs an oversampling of background seeds and calculates an optimum-path forest rooted at the seeds, removing the less relevant ones at each iteration. For seed removal, we propose two strategies, in which the first considers the distance from superpixels to the scribbles, and the second takes into account the superpixel position on the object’s border. Alongside this, we propose three connectivity functions with a dynamic trees approach considering the local gradient and the contrast of the image.

Due to the background seed oversampling, iDISF increases the seed competition and, therefore, is capable of preventing major object and background leaks, and, therefore, achieving better delineation than the baseline. The images show that inclusion of gradient information can further improve delineation by preventing leakings.