Multi-field Visualisation via Trait-induced Merge Trees

• URL: http://arxiv.org/abs/2308.09015v1
• Date: Thu, 17 Aug 2023 14:40:48 GMT
• Title: Multi-field Visualisation via Trait-induced Merge Trees
• Authors: Jochen Jankowai, Talha Bin Masood, and Ingrid Hotz
• Abstract summary: We employ the notion of traits defined in attribute space as introduced in the feature level sets framework. The resulting distance field in attribute space induces a scalar field in the spatial domain that serves as input for topological data analysis. The presented method includes different query methods for the tree which enable the highlighting of different aspects.
• Score: 3.7332349900024013
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we propose trait-based merge trees a generalization of merge trees to feature level sets, targeting the analysis of tensor field or general multi-variate data. For this, we employ the notion of traits defined in attribute space as introduced in the feature level sets framework. The resulting distance field in attribute space induces a scalar field in the spatial domain that serves as input for topological data analysis. The leaves in the merge tree represent those areas in the input data that are closest to the defined trait and thus most closely resemble the defined feature. Hence, the merge tree yields a hierarchy of features that allows for querying the most relevant and persistent features. The presented method includes different query methods for the tree which enable the highlighting of different aspects. We demonstrate the cross-application capabilities of this approach with three case studies from different domains.

Related papers

• ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval [64.44265315244579]
  We propose a tree-based method for organizing and representing reference documents at various granular levels. Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches. Our evaluations show that ReTreever generally preserves full representation accuracy.
  arXiv  Detail & Related papers  (2025-02-11T21:35:13Z)
• Multi-field Visualization: Trait design and trait-induced merge trees [2.862576303934634]
  Feature level sets (FLS) have shown significant potential in the analysis of multi-field data by using traits defined in attribute space to specify features. In this work, we address key challenges in the practical use of FLS: trait design and feature selection for rendering. We propose a decomposition of traits into simpler components, making the process more intuitive and computationally efficient.
  arXiv  Detail & Related papers  (2025-01-08T10:13:32Z)
• 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)
• AdaTreeFormer: Few Shot Domain Adaptation for Tree Counting from a Single High-Resolution Image [11.649568595318307]
  This paper proposes a framework that is learnt from the source domain with sufficient labeled trees. It is adapted to the target domain with only a limited number of labeled trees. Experimental results show that AdaTreeFormer significantly surpasses the state of the art.
  arXiv  Detail & Related papers  (2024-02-05T12:34:03Z)
• UniVIE: A Unified Label Space Approach to Visual Information Extraction from Form-like Documents [11.761942458294136]
  We present a new perspective, reframing VIE as a relation prediction problem and unifying labels of different tasks into a single label space. This unified approach allows for the definition of various relation types and effectively tackles hierarchical relationships in form-like documents. We present UniVIE, a unified model that addresses the VIE problem comprehensively.
  arXiv  Detail & Related papers  (2024-01-17T14:02:36Z)
• Benchmarking Individual Tree Mapping with Sub-meter Imagery [6.907098367807166]
  We introduce an evaluation framework suited for individual tree mapping in any physical environment. We review and compare different approaches and deep architectures, and introduce a new method that we experimentally prove to be a good compromise between segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-14T08:21:36Z)
• Hierarchical Matching and Reasoning for Multi-Query Image Retrieval [113.44470784756308]
  We propose a novel Hierarchical Matching and Reasoning Network (HMRN) for Multi-Query Image Retrieval (MQIR) It disentangles MQIR into three hierarchical semantic representations, which is responsible to capture fine-grained local details, contextual global scopes, and high-level inherent correlations. Our HMRN substantially surpasses the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-26T07:03:56Z)
• Hierarchical clustering with dot products recovers hidden tree structure [53.68551192799585]
  In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance. We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic graphical model.
  arXiv  Detail & Related papers  (2023-05-24T11:05:12Z)
• Intersection Regularization for Extracting Semantic Attributes [72.53481390411173]
  We consider the problem of supervised classification, such that the features that the network extracts match an unseen set of semantic attributes. For example, when learning to classify images of birds into species, we would like to observe the emergence of features that zoologists use to classify birds. We propose training a neural network with discrete top-level activations, which is followed by a multi-layered perceptron (MLP) and a parallel decision tree.
  arXiv  Detail & Related papers  (2021-03-22T14:32:44Z)
• Rethinking Learnable Tree Filter for Generic Feature Transform [71.77463476808585]
  Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. To relax the geometric constraint, we give the analysis by reformulating it as a Markov Random Field and introduce a learnable unary term. For semantic segmentation, we achieve leading performance (82.1% mIoU) on the Cityscapes benchmark without bells-and-whistles.
  arXiv  Detail & Related papers  (2020-12-07T07:16:47Z)
• Measure Inducing Classification and Regression Trees for Functional Data [0.0]
  We propose a tree-based algorithm for classification and regression problems in the context of functional data analysis. This is achieved by learning a weighted functional $L2$ space by means of constrained convex optimization.
  arXiv  Detail & Related papers  (2020-10-30T18:49:53Z)

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.