MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection

2021-06-01

Computer Vision and Pattern Recognition

AI Summary

This paper addresses the robust joint recovery of multiple geometric structures (e.g., planes, cylinders, homography/fundamental matrices) from noisy data contaminated with outliers. We propose an online model fitting and selection-driven agglomerative clustering framework. Our method innovatively integrates dynamic linkage criteria to enable end-to-end co-optimization of model fitting and selection. It combines online RANSAC-style fitting, information-theoretic adaptive model selection, and multi-structure consistency metrics—thereby overcoming key limitations of conventional approaches, including sensitivity to inlier thresholds and severe sampling bias. Extensive evaluations on multiple public benchmarks demonstrate significant improvements over state-of-the-art methods, achieving high accuracy, strong robustness to outliers and noise, fast runtime, and insensitivity to threshold tuning. The source code is publicly available.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that MultiLink favourably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download1.

Problem

• Recover multiple geometric structures from noisy data
• Robust fitting for mixed parametric model classes
• Simultaneous handling of multi-class model recovery

Innovation

• Agglomerative clustering for multi-class structure recovery
• On-the-fly model fitting and selection
• Novel linkage scheme for cluster merging