gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Discovery of Frequent Correlated Subgraph Pairs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Image and Vision Computing
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
Hi-index | 0.00 |
A significant amount of research in robotics is aimed towards building robots that operate indoors yet there exists little analysis of how human spaces are organized. In this work we analyze the properties of indoor environments from a large annotated floorplan dataset. We analyze a corpus of 567 floors, 6426 spaces with 91 room types and 8446 connections between rooms corresponding to real places. We present a system that, given a partial graph, predicts the rest of the topology by building a model from this dataset. Our hypothesis is that indoor topologies consists of multiple smaller functional parts. We demonstrate the applicability of our approach with experimental results. We expect that our analysis paves the way for more data driven research on indoor environments.