← 研踪 PaperTrailCVF · CVPR 2026:cvf:7eef1cd895d8d69f261b2bc507d27cff · 记录 #1
SegGBC: Justifiable Coarse-to-Fine Granular-Ball Computing for Enhancing Clustering Image Segmentation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:39
摘要
As an emerging multi-granularity clustering paradigm, granular-ball computing (GBC) hierarchically represents samples through granular-balls (GBs) to capture compact, multi-scale features. Nevertheless, its effective application to clustering-based segmentation methods (CSMs) remains challenging due to two key issues: representing intrinsic uncertainties and defining a justifiable, semantics-aware quality criterion. To address them, the first segmentation framework based on GBC (SegGBC) is proposed to alleviate the single-granularity limitation of existing CSMs. Concretely, we leverage intuitionistic fuzzy sets (IFS) to explicitly quantify image uncertainty: membership and non-membership encode evidence, and the IFS hesitation degree models residual ambiguity. In addition, a semantic compactness metric criterion (SCM_GB) is designed to characterize semantic information by considering the "stable region" in conjunction with the overall density of GBs. The proposal of "stable region" ensures robust semantics concurrently with high computational efficiency. Extensive experiments demonstrate that the proposed SegGBC achieves promising performance for segmentation. The proposed segmentation GB representation is a plug-and-play front-end, significantly boosting the performance of CSMs by >+3.25% SA and >+3.92% mIoU on standard image and COCO benchmarks. Code is available at supplementary material.
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记录 #1 · 2026 · 最新观测版本
SegGBC: Justifiable Coarse-to-Fine Granular-Ball Computing for Enhancing Clustering Image Segmentation
As an emerging multi-granularity clustering paradigm, granular-ball computing (GBC) hierarchically represents samples through granular-balls (GBs) to capture compact, multi-scale features. Nevertheless, its effective application to clustering-based segmentation methods (CSMs) remains challenging due to two key issues: representing intrinsic uncertainties and defining a justifiable, semantics-aware quality criterion. To address them, the first segmentation framework based on GBC (SegGBC) is proposed to alleviate the single-granularity limitation of existing CSMs. Concretely, we leverage intuitionistic fuzzy sets (IFS) to explicitly quantify image uncertainty: membership and non-membership encode evidence, and the IFS hesitation degree models residual ambiguity. In addition, a semantic compactness metric criterion (SCM_GB) is designed to characterize semantic information by considering the "stable region" in conjunction with the overall density of GBs. The proposal of "stable region" ensures robust semantics concurrently with high computational efficiency. Extensive experiments demonstrate that the proposed SegGBC achieves promising performance for segmentation. The proposed segmentation GB representation is a plug-and-play front-end, significantly boosting the performance of CSMs by >+3.25% SA and >+3.92% mIoU on standard image and COCO benchmarks. Code is available at supplementary material.
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