Efficient Multiscale Object-based Superpixel Framework

• URL: http://arxiv.org/abs/2204.03533v1
• Date: Thu, 7 Apr 2022 15:59:38 GMT
• Title: Efficient Multiscale Object-based Superpixel Framework
• Authors: Felipe Bel\'em and Benjamin Perret and Jean Cousty and Silvio J. F. Guimar\~aes and Alexandre Falc\~ao
• Abstract summary: We propose a novel superpixel framework, named Superpixels through Iterative CLEarcutting (SICLE) SICLE exploits object information being able to generate a multiscale segmentation on-the-fly. It generalizes recent superpixel methods, surpassing them and other state-of-the-art approaches in efficiency and effectiveness according to multiple delineation metrics.
• Score: 62.48475585798724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Superpixel segmentation can be used as an intermediary step in many applications, often to improve object delineation and reduce computer workload. However, classical methods do not incorporate information about the desired object. Deep-learning-based approaches consider object information, but their delineation performance depends on data annotation. Additionally, the computational time of object-based methods is usually much higher than desired. In this work, we propose a novel superpixel framework, named Superpixels through Iterative CLEarcutting (SICLE), which exploits object information being able to generate a multiscale segmentation on-the-fly. SICLE starts off from seed oversampling and repeats optimal connectivity-based superpixel delineation and object-based seed removal until a desired number of superpixels is reached. It generalizes recent superpixel methods, surpassing them and other state-of-the-art approaches in efficiency and effectiveness according to multiple delineation metrics.

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