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Multimedia Systems
Erschienen in: Multimedia Systems 1/2017

24.03.2015 | Special Issue Paper

A discriminative graph inferring framework towards weakly supervised image parsing

verfasst von: Lei Yu, Bing-Kun Bao, Changsheng Xu

Erschienen in: Multimedia Systems | Ausgabe 1/2017

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Abstract

In this paper, we focus on the task of assigning labels to the over-segmented image patches in a weakly supervised manner, in which the training images contain the labels but do not have the labels’ locations in the images. We propose a unified discriminative graph inferring framework by simultaneously inferring patch labels and learning the patch appearance models. On one hand, graph inferring reasons the patch labels by a graph propagation procedure. The graph is constructed by connecting the nearest neighbors which share the same image label, and multiple correlations among patches and image labels are imposed as constraints to the inferring. On the other hand, for each label, the patches which do not contain the target label are adopted as negative samples to learn the appearance model. In this way, the predicted labels will be more accurate in the propagation. Graph inferring and the learned patch appearance models are finally embedded to complement each other in one unified formulation. Experiments on three public datasets demonstrate the effectiveness of our method in comparison with other baselines.
Vorheriger Artikel Special issue on “visual semantic analysis with weak supervision”
Nächster Artikel 3D Object retrieval based on viewpoint segmentation

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Metadaten
Titel
A discriminative graph inferring framework towards weakly supervised image parsing
verfasst von
Lei Yu
Bing-Kun Bao
Changsheng Xu
Publikationsdatum
24.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 1/2017
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-015-0458-5

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