Some SuperHyperGraph-Based Concepts: Image segmentation, Text representation, and SLAM
Takaaki Fujita · 2025
Graph theory has been applied across a wide range of fields [1, 2]. Hypergraphs extend classical graphs by allowing hyperedges to connect arbitrary subsets of vertices, while superhypergraphs further generalize this framework by incorporating iterated powersets that capture hierarchical and self-referential connections [3, 4]. Although graph-based models have been widely studied and applied in practice, this paper explores how Graph-Based Image Segmentation, Text Representation, and SLAM (Simultaneous Localization and Mapping) can be extended using hypergraph and superhypergraph structures. Such extensions may provide significant advantages, including the ability to apply these methods to hierarchical concepts, thereby enhancing the interpretability and performance of image segmentation, text modeling, and SLAM in complex settings.