# Interactive multiclass segmentation using superpixel classification

arXiv: https://arxiv.org/abs/1510.03199 · HTML: https://mlsanity.com/paper/1510.03199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2015.

## Built on by (most cited first, 1 of 1)

- [Geometric Active Learning for Segmentation of Large 3D Volumes](https://mlsanity.com/paper/2210.06885/) (2022)

## Abstract (arXiv)

This paper adresses the problem of interactive multiclass segmentation. We propose a fast and efficient new interactive segmentation method called Superpixel Classification-based Interactive Segmentation (SCIS). From a few strokes drawn by a human user over an image, this method extracts relevant semantic objects. To get a fast calculation and an accurate segmentation, SCIS uses superpixel over-segmentation and support vector machine classification. In this paper, we demonstrate that SCIS significantly outperfoms competing algorithms by evaluating its performances on the reference benchmarks of McGuinness and Santner.