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Graph-based Representation for Image based on Granular-ball

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arxiv 2303.02388 v1 pith:TWYBXYPY submitted 2023-03-04 cs.CV eess.IV

Xia Shuyin , Dai Dawei , Yang Long , Zhany Li , Lan Danf , Zhu hao , Wang Guoy This is my paper · ORCID
classification cs.CVeess.IV
keywords imagenodenodesrepresentationdesignedgeefficiencygranular-ball
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Current image processing methods usually operate on the finest-granularity unit; that is, the pixel, which leads to challenges in terms of efficiency, robustness, and understandability in deep learning models. We present an improved granular-ball computing method to represent the image as a graph, in which each node expresses a structural block in the image and each edge represents the association between two nodes. Specifically:(1) We design a gradient-based strategy for the adaptive reorganization of all pixels in the image into numerous rectangular regions, each of which can be regarded as one node. (2) Each node has a connection edge with the nodes with which it shares regions. (3) We design a low-dimensional vector as the attribute of each node. All nodes and their corresponding edges form a graphical representation of a digital image. In the experiments, our proposed graph representation is applied to benchmark datasets for image classification tasks, and the efficiency and good understandability demonstrate that our proposed method offers significant potential in artificial intelligence theory and application.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction

    cs.SI 2026-07 conditional novelty 6.0 of 10

    MGLP uses granular-ball graph refinement to construct hierarchical landmarks and a depth-weighted distance measure, improving link prediction over single-granularity position embeddings.

  2. 3DGBGS: 3D Granular Ball Gaussian Splatting for Compact Novel View Synthesis

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Using granular-ball point clusters to initialize anchors and Gaussian scales reduces 3D Gaussian Splatting model size by about 10% with near-identical rendering quality.

  3. Approximate Borderline Sampling using Granular-Ball for Classification Tasks

    cs.CV 2025-06 conditional novelty 6.0 of 10

    GBABS samples only approximate borderline points detected via non-overlapping granular balls, reporting better classifier accuracy and noise robustness than GB-based and standard sampling baselines.

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