Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
ACM Computing Surveys (CSUR)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Random Subwindows for Robust Image Classification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Machine Learning
Kernelizing the output of tree-based methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Image retrieval based on multi-texton histogram
Pattern Recognition
Image retrieval with semantic sketches
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part I
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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.