Interactive segmentation: a scalable superpixel-based method

Bérengère Mathieu; Alain Crouzil; Jean-Baptiste Puel

doi:10.1117/1.JEI.26.6.061606

26 September 2017 Interactive segmentation: a scalable superpixel-based method

Bérengère Mathieu, Alain Crouzil, Jean-Baptiste Puel

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 061606 (September 2017). https://doi.org/10.1117/1.JEI.26.6.061606

Abstract

This paper addresses the problem of interactive multiclass segmentation of images. We propose a fast and efficient new interactive segmentation method called superpixel α fusion (SαF). From a few strokes drawn by a user over an image, this method extracts relevant semantic objects. To get a fast calculation and an accurate segmentation, SαF uses superpixel oversegmentation and support vector machine classification. We compare SαF with competing algorithms by evaluating its performances on reference benchmarks. We also suggest four new datasets to evaluate the scalability of interactive segmentation methods, using images from some thousand to several million pixels. We conclude with two applications of SαF.

Citation Download Citation

Bérengère Mathieu, Alain Crouzil, and Jean-Baptiste Puel "Interactive segmentation: a scalable superpixel-based method," Journal of Electronic Imaging 26(6), 061606 (26 September 2017). https://doi.org/10.1117/1.JEI.26.6.061606

Received: 1 April 2017; Accepted: 30 August 2017; Published: 26 September 2017

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