Interactive multiclass segmentation using superpixel classification

TL;DR

This paper introduces SCIS, a fast and accurate interactive multiclass segmentation method that leverages superpixel over-segmentation and SVM classification to efficiently extract semantic objects from minimal user input.

Contribution

The paper presents a novel segmentation approach combining superpixel over-segmentation with SVMs, outperforming existing methods in speed and accuracy.

Findings

SCIS achieves higher accuracy than competing algorithms.

SCIS significantly reduces computation time.

Demonstrated superior performance on benchmark datasets.

Abstract

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.