Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

We propose a new method for content-based image retrieval
which exploits the similarity measure and indexing structure of totally
randomized tree ensembles induced from a set of subwindows randomly
extracted from a sample of images. We also present the possibility of
updating the model as new images come in, and the capability of comparing
new images using a model previously constructed from a different
set of images. The approach is quantitatively evaluated on various types
of images with state-of-the-art results despite its conceptual simplicity
and computational efficiency