# Geodesic Distance Histogram Feature for Video Segmentation

arXiv: https://arxiv.org/abs/1704.00077 · HTML: https://mlsanity.com/paper/1704.00077/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2017.

## Built on by (most cited first, 6 of 6)

- [From Shadow Generation to Shadow Removal](https://mlsanity.com/paper/2103.12997/) (2021)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [Temporal Feature Warping for Video Shadow Detection](https://mlsanity.com/paper/2107.14287/) (2021)
- [GeoMM: On Geodesic Perspective for Multi-modal Learning](https://mlsanity.com/paper/2505.11216/) (2025)
- [A Feature Generator for Few-Shot Learning](https://mlsanity.com/paper/2409.14141/) (2024)
- [Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning](https://mlsanity.com/paper/2512.06179/) (2025)

## Abstract (arXiv)

This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets.