Large-scale image segmentation based on distributed clustering algorithms

• URL: http://arxiv.org/abs/2106.10795v1
• Date: Mon, 21 Jun 2021 01:11:49 GMT
• Title: Large-scale image segmentation based on distributed clustering algorithms
• Authors: Ran Lu, Aleksandar Zlateski and H. Sebastian Seung
• Abstract summary: Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions. Here we describe a distributed algorithm capable of handling a tremendous number of supervoxels. We demonstrate the algorithm by clustering an affinity graph with over 1.5 trillion edges between 135 billion supervoxels derived from a 3D electron microscopic brain image.
• Score: 70.8481702473572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions. Here we describe a distributed algorithm capable of handling a tremendous number of supervoxels. The algorithm works recursively, the regions are divided into chunks that are processed independently in parallel by multiple workers. At each round of the recursive procedure, the chunk size in all dimensions are doubled until a single chunk encompasses the entire image. The final result is provably independent of the chunking scheme, and the same as if the entire image were processed without division into chunks. This is nontrivial because a pair of adjacent regions is scored by some statistical property (e.g. mean or median) of the affinities at the interface, and the interface may extend over arbitrarily many chunks. The trick is to delay merge decisions for regions that touch chunk boundaries, and only complete them in a later round after the regions are fully contained within a chunk. We demonstrate the algorithm by clustering an affinity graph with over 1.5 trillion edges between 135 billion supervoxels derived from a 3D electron microscopic brain image.

Related papers

• Parallel Watershed Partitioning: GPU-Based Hierarchical Image Segmentation [4.560718678349679]
  Many image processing applications rely on partitioning an image into disjoint regions whose pixels are'similar' We show how to use this fully deterministic image partitioning as a pre-processing step to machine learning based semantic segmentation.
  arXiv  Detail & Related papers  (2024-10-11T16:15:43Z)
• View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields [52.08335264414515]
  We learn a novel feature field within a Neural Radiance Field (NeRF) representing a 3D scene. Our method takes view-inconsistent multi-granularity 2D segmentations as input and produces a hierarchy of 3D-consistent segmentations as output. We evaluate our method and several baselines on synthetic datasets with multi-view images and multi-granular segmentation, showcasing improved accuracy and viewpoint-consistency.
  arXiv  Detail & Related papers  (2024-05-30T04:14:58Z)
• Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections [58.720142291102135]
  We propose a new iterative method which we term Iterative Next Boundary Detection (INBD) It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order.
  arXiv  Detail & Related papers  (2022-12-06T14:49:41Z)
• Counting Objects by Diffused Index: geometry-free and training-free approach [0.0]
  We propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation.
  arXiv  Detail & Related papers  (2021-10-15T20:53:37Z)
• A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation [9.910277829897744]
  Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. This paper proposes a divide-and-merge LiDAR clustering algorithm. It is implemented with C++ and wrapped as a python function.
  arXiv  Detail & Related papers  (2021-09-16T21:15:25Z)
• A Novel Falling-Ball Algorithm for Image Segmentation [0.14337588659482517]
  Region-based Falling-Ball algorithm is presented, which is a region-based segmentation algorithm. The proposed algorithm detects the catchment basins by assuming that a ball falling from hilly terrains will stop in a catchment basin.
  arXiv  Detail & Related papers  (2021-05-06T12:41:10Z)
• InfinityGAN: Towards Infinite-Resolution Image Synthesis [92.40782797030977]
  We present InfinityGAN, a method to generate arbitrary-resolution images. We show how it trains and infers patch-by-patch seamlessly with low computational resources.
  arXiv  Detail & Related papers  (2021-04-08T17:59:30Z)
• Superpixel Segmentation Based on Spatially Constrained Subspace Clustering [57.76302397774641]
  We consider each representative region with independent semantic information as a subspace, and formulate superpixel segmentation as a subspace clustering problem. We show that a simple integration of superpixel segmentation with the conventional subspace clustering does not effectively work due to the spatial correlation of the pixels. We propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel.
  arXiv  Detail & Related papers  (2020-12-11T06:18:36Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)
• The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation [15.768804877756384]
  We propose a greedy algorithm for joint graph partitioning and labeling. Due to the algorithm's efficiency it can operate directly on pixels without prior over-segmentation of the image into superpixels.
  arXiv  Detail & Related papers  (2019-12-29T19:48:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.