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Multi-class Model Fitting by Energy Minimization and Mode-Seeking

  • Daniel Barath
  • Jiri Matas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

Abstract

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting – the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used \(\alpha \)-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a cross-validation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the state-of-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting.

Keywords

Multi-model fitting Clustering Energy minimization 

Notes

Acknowledgement

The authors were supported by the Czech Science Foundation Project GACR P103/12/G084.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Centre for Machine Perception, Department of CyberneticsCzech Technical UniversityPragueCzech Republic
  2. 2.Machine Perception Research LaboratoryMTA SZTAKIBudapestHungary

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