2012 | OriginalPaper | Buchkapitel
Ensemble Partitioning for Unsupervised Image Categorization
verfasst von : Dengxin Dai, Mukta Prasad, Christian Leistner, Luc Van Gool
Erschienen in: Computer Vision – ECCV 2012
Verlag: Springer Berlin Heidelberg
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While the quality of object recognition systems can strongly benefit from more data, human annotation and labeling can hardly keep pace. This motivates the usage of autonomous and unsupervised learning methods. In this paper, we present a simple, yet effective method for unsupervised image categorization, which relies on discriminative learners. Since automatically obtaining error-free labeled training data for the learners is infeasible, we propose the concept of weak training (
WT
) set.
WT
sets have various deficiencies, but still carry useful information. Training on a single
WT
set cannot result in good performance, thus we design a random walk sampling scheme to create a series of diverse
WT
sets. This naturally allows our categorization learning to leverage ensemble learning techniques. In particular, for each
WT
set, we train a max-margin classifier to further partition the whole dataset to be categorized. By doing so, each
WT
set leads to a base partitioning of the dataset and all the base partitionings are combined into an ensemble proximity matrix. The final categorization is completed by feeding this proximity matrix into a spectral clustering algorithm. Experiments on a variety of challenging datasets show that our method outperforms competing methods by a considerable margin.