Object boundary detection and classification with image-level labels

Jing Yu Koh, Wojciech Samek, Klaus Robert Müller, Alexander Binder

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Semantic boundary and edge detection aims at simultaneously detecting object edge pixels in images and assigning class labels to them. Systematic training of predictors for this task requires the labeling of edges in images which is a particularly tedious task. We propose a novel strategy for solving this task, when pixel-level annotations are not available, performing it in an almost zero-shot manner by relying on conventional whole image neural net classifiers that were trained using large bounding boxes. Our method performs the following two steps at test time. Firstly it predicts the class labels by applying the trained whole image network to the test images. Secondly, it computes pixel-wise scores from the obtained predictions by applying backprop gradients as well as recent visualization algorithms such as deconvolution and layer-wise relevance propagation. We show that high pixel-wise scores are indicative for the location of semantic boundaries, which suggests that the semantic boundary problem can be approached without using edge labels during the training phase.

Original languageEnglish
Title of host publicationPattern Recognition - 39th German Conference, GCPR 2017, Proceedings
EditorsVolker Roth, Thomas Vetter
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319667089
Publication statusPublished - 2017
Event39th German Conference on Pattern Recognition, GCPR 2017 - Basel, Switzerland
Duration: 2017 Sept 122017 Sept 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10496 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other39th German Conference on Pattern Recognition, GCPR 2017

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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