Web Image Retrieval Re-Ranking with Relevance Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ImprovingWeb-based Image Search via Content Based Clustering
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A New Color-Namiing System for Graphics Languages
IEEE Computer Graphics and Applications
Color and texture image retrieval using chromaticity histograms and wavelet frames
IEEE Transactions on Multimedia
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With web image search engines, we face a situation where the results are very noisy, and when we ask for a specific object, we are not ensured that this object is contained in all the images returned by the search engines: about 50% of the images returned are off-topic. In this paper, we explain how knowing the color of an object can help locating the object in images, and we also propose methods to automatically find the color of an object, so that the whole process can be fully automatic. Results reveal that this method allows us to reduce the noise in returned images while providing automatic segmentation so that it can be used for clustering or object learning.