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Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels

  • Josip KrapacEmail author
  • Siniša Šegvić
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

We address the problem of semantic segmentation of objects in weakly supervised setting, when only image-wide labels are available. We describe an image with a set of pre-trained convolutional features and embed this set into a Fisher vector. We apply the learned image classifier on the set of all image regions and propagate the region scores back to the pixels. Compared to the alternatives the proposed method is simple, fast in inference, and especially in training. The method displays very good performance of on two standard semantic segmentation benchmarks.

Keywords

Convolutional Neural Network Conditional Random Field Segmentation Performance Fisher Vector Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work has been fully supported by Croatian Science Foundation under the project I-2433-2014.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

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