Fast and Effective Superpixel Segmentation Using Accurate Saliency Estimation

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AbstractSuperpixels through Iterative CLEarcutting (SICLE) is a recently proposed framework for superpixel segmentation. SICLE consists of three steps: (i) seed oversampling; (ii) superpixel generation; and (iii) seed removal; such that, after step (i), steps (ii) and (iii) are repeated until a desired number of superpixels is obtained. Such pipeline showed effective and efficient multiscale superpixel segmentation. Furthermore, if an object is desired, it is possible to improve delineation by providing its probable location, often called saliency. While classical methods estimate object saliency by contrast-based criteria, recent ones use deep-learning strategies for accurate estimation. SICLE shows robustness for low-quality saliency estimations, but it struggles to effectively take advantage of the high-quality ones. In this work, we propose a generalization of its path-cost function and seed removal criterion (steps (ii) and (iii), respectively), adapting SICLE to a given saliency map. By choice of a binary parameter, SICLE can take advantage of low- and high-quality saliency maps for better segmentation. Results show that, by exploiting the accurate information of the saliency map, our improved SICLE version surpasses state-of-the-art methods in traditional delineation metrics while requiring only two iterations for segmentation, being significantly faster than its predecessor and SLIC.KeywordsSuperpixel segmentationObject saliency mapImage foresting transform