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Multimedia Systems
Erschienen in: Multimedia Systems 1/2013

01.02.2013 | Regular Paper

Weakly-supervised object localization in unlabeled image collection

verfasst von: Yanyun Qu, Han Liu, Xiaoqing Yang, Suwen Fang, Hanzi Wang

Erschienen in: Multimedia Systems | Ausgabe 1/2013

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Abstract

Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images.
Vorheriger Artikel Combining global and local matching of multiple features for precise item image retrieval
Nächster Artikel Robust wireless sharing of internet video streams

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Metadaten
Titel
Weakly-supervised object localization in unlabeled image collection
verfasst von
Yanyun Qu
Han Liu
Xiaoqing Yang
Suwen Fang
Hanzi Wang
Publikationsdatum
01.02.2013
Verlag
Springer-Verlag
Erschienen in
Multimedia Systems / Ausgabe 1/2013
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-012-0293-x

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