Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
This addresses robust model fitting in computer vision, offering improved performance for applications like image analysis, but appears incremental as it builds on existing mode-seeking and hypergraph concepts.
The paper tackles robust geometric model fitting for multi-structure data with severe outliers by proposing Mode-Seeking on Hypergraphs (MSH), which formulates the problem as mode seeking on a hypergraph and demonstrates significant superiority over state-of-the-art methods in experiments on synthetic data and real images.
In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH),to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a weight-aware sampling technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.