The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
Towards Scalable Dataset Construction: An Active Learning Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Can social tagged images aid concept-based video search?
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
Learning automatic concept detectors from online video
Computer Vision and Image Understanding
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Several branches of computer vision heavily rely (but we could even say depend) on the availability of large datasets of labelled images. While such labeling is usually done by hand, a powerful help can be obtained from Internet and its related tools. In this paper we address the problem of automatically generating a set of images representing an object class, given the name of the class. We exploit semantic technologies, such as lexical resources and ontologies, in order to improve the search performances by using a standard web search engine. We will also discuss an application to the automatic building of a training set for a classification framework. Preliminary experiments are provided for 10 classes from the public CalTech256 dataset and results show an average increment in classification accuracy of about 10%.