Flexible Multi-modal Graph-Based Segmentation

This paper aims at improving the well-known local variance segmentation method by adding extra signal modi and specific processing steps. As a key contribution, we extend the uni-modal segmentation method to perform multi-modal analysis, such that any number of signal modi available can be incorporated in a very flexible way. We have found that the use of a combined weight of luminance and depth values improves the segmentation score by 6.8%, for a large and challenging multi-modal dataset. Furthermore, we have developed an improved uni-modal texture-segmentation algorithm. This improvement relies on a clever choice of the color space and additional preand post-processing steps, by which we have increased the segmentation score on a challenging texture dataset by 2.1%. This gain is mainly preserved when using a different dataset with worse lighting conditions and different scene types.