The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Generic image classification using visual knowledge on the web
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A bootstrapping framework for annotating and retrieving WWW images
Proceedings of the 12th annual ACM international conference on Multimedia
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Visual pattern discovery using web images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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We propose a new Web image gathering system which employs a part-based object recognition method. The novelty of our work is introducing the bag-of-keypoints representation into an Web image gathering task instead of color histogram or segmented regions our previous system used. The bag-of-keypoints representation has been proven that it has the excellent ability to represent image concepts in the context of visual object categorization / recognition in spite of its simplicity. Most of object recognition work assumed that complete training data is available. On the other hand, in the Web image gathering task, since images associated with the given keywords are gathered from the Web fully-automatically, complete training images cannot be available. In this paper, we combine the HTML-based automatic positive training image selection and the bag-of-keypoints-based image selection with an SVM which is a supervised machine learning method. This combination enables the system to gather many images related to given concepts with high precision fully automatically needing no human intervention. Ourmain objective is to examine if the bag-of-keypointsmodel is also effective for theWeb image gathering taskwhere training images always include some noise. By the experiments, we show the new system outperforms our previous systems, other systems and Google Image Search greatly.