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 ?-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