Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Discriminative Object Class Models of Appearance and Shape by Correlatons
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Visual pattern discovery for architecture image classification and product image search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Conventional object recognition techniques rely heavily on manually annotated image datasets to achieve good performances. However, collecting high quality datasets is really laborious. In this paper, we propose a semi-supervised framework for learning visual categories from Google Images. The 1st and 2nd order features, which define bag of words representation and spatial relationship between local features respectively, make up an independent and redundant feature split. We then integrate a cotraining algorithm CoBoost with these two features. We create two boosting classifiers based on the 1st and 2nd order features respectively in the training, during which one classifier provides labels for the other. Besides, the 2nd order features are generated dynamically rather than extracted exhaustively to avoid high computation. An active learning technique is also introduced to further improve the performance. We evaluate our method on the benchmark datasets, showing results competitive with the state-of-the-art unsupervised approaches and some supervised techniques.