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Learning discriminative dictionaries with partially labeled data
Ist Teil von
2012 19th IEEE International Conference on Image Processing, 2012, p.3113-3116
Ort / Verlag
IEEE
Erscheinungsjahr
2012
Quelle
IEEE Electronic Library (IEL)
Beschreibungen/Notizen
While recent techniques for discriminative dictionary learning have demonstrated tremendous success in image analysis applications, their performance is often limited by the amount of labeled data available for training. Even though labeling images is difficult, it is relatively easy to collect unlabeled images either by querying the web or from public datasets. In this paper, we propose a discriminative dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries. Extensive evaluation on existing datasets demonstrate that the proposed method performs significantly better than state of the art dictionary learning approaches when unlabeled images are available for training.