TY - GEN
T1 - Object boundary detection and classification with image-level labels
AU - Koh, Jing Yu
AU - Samek, Wojciech
AU - Müller, Klaus Robert
AU - Binder, Alexander
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029594122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029594122&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66709-6_13
DO - 10.1007/978-3-319-66709-6_13
M3 - Conference contribution
AN - SCOPUS:85029594122
SN - 9783319667089
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 164
BT - Pattern Recognition - 39th German Conference, GCPR 2017, Proceedings
A2 - Roth, Volker
A2 - Vetter, Thomas
PB - Springer Verlag
T2 - 39th German Conference on Pattern Recognition, GCPR 2017
Y2 - 12 September 2017 through 15 September 2017
ER -