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.
Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
2007-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Image Retrieval , Scalable Invariant Feature Transform , Training Image , Query Image , Recognition Rate Pattern Recognition , Artificial Intelligence , Algorithm Analysis and Problem Complexity , Computer Science , Biometrics , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics
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