@inproceedings {501,
	title = {Fast retrieval methods for images with significant variations},
	booktitle = {International Conference on Image Processing},
	year = {2000},
	abstract = {<p>Fast image retrieval is the key to success for operations on large image databases, and a great many techniques have been developed for efficient retrieval. However, most of these methods are tailored to visual scenes or to images having limited variations. We investigate the searching of enormous databases (of up to 10 7 images) for the matching and identification of precious stones (principally diamonds). Because of the size of the database, we propose a hierarchy of classifiers, which successively prune candidate images such that the more complex classifiers are required to test only tiny portions of the data. The new classifier developed here applies a wavelet transform to image histograms and is capable of rejecting 99.9\% of bad matches.</p>
},
	keywords = {classifiers hierarchy, database searching, diamond, diamonds, fast image retrieval methods, image classification, image histograms, image matching, image retrieval, large image databases, precious stones identification, precious stones matching, visual databases, visual scenes, wavelet transform, wavelet transforms},
	doi = {http://dx.doi.org/10.1109/ICIP.2000.899470},
	author = {P Fieguth and R Wan}
}
