How to scale hyperparameters for quickshift image segmentation
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.
Code (1)
Tasks
Image SegmentationSemantic SegmentationSuperpixelsSimilar Papers 제목 키워드 기반
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes …
ClusteringImage SegmentationSemantic SegmentationPre-Clustering Point Clouds of Crop Fields Using Scalable Methods
In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop field…
ClusteringPlant PhenotypingSDiT: Semantic Region-Adaptive for Diffusion Transformers
Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterative nature of denoising and the quadratic cost of global attention. In t…
Survey of Image Based Graph Neural Networks
In this survey paper, we analyze image based graph neural networks and propose a three-step classification approach. We first convert the image into superpixels using the Quickshift algorithm so as to reduce 30% of the i…
ClassificationSuperpixelsSurveyMSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation
The nnUNet segmentation framework adeptly adjusts most hyperparameters in training scripts automatically, but it overlooks the tuning of internal hyperparameters within the segmentation network itself, which constrains t…
Medical Image SegmentationSkin Lesion Segmentation