2022
AISTATS
AISTATS 2022
How to scale hyperparameters for quickshift image segmentation
Abstract
Quickshift is a popular algorithm for image segmentation, used as a preprocessing step in many applications. Unfortunately, it is quite challenging to understand the hyperparameters’ influence on the number and shape of superpixels produced by the method. In this paper, we study theoretically a slightly modified version of the quickshift algorithm, with a particular emphasis on homogeneous image patches with i.i.d. pixel noise and sharp boundaries between such patches. Leveraging this analysis, we derive a simple heuristic to scale quickshift hyperparameters with respect to the image size, which we check empirically.
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Interdisciplinary Bridge
— Computer Vision and Machine Learning
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Trend Setter
— Image Segmentation
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Keyword Pioneer
— hyperparameter scaling
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning