Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Diagnosing extrapolation: tree-based density estimation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The Amsterdam Library of Object Images
International Journal of Computer Vision
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised induction of labeled parse trees by clustering with syntactic features
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Steerable Features for Statistical 3D Dendrite Detection
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
The quadratic-chi histogram distance family
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Unsupervised discretization using tree-based density estimation
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Hi-index | 0.10 |
This paper offers a methodological contribution for computing the distance between two empirical distributions in an Euclidean space of very large dimension. We propose to use decision trees instead of relying on standard quantification of the feature space. Our contribution is twofold: We first define a new distance between empirical distributions, based on the Kullback-Leibler (KL) divergence between the distributions over the leaves of decision trees built for the two empirical distributions. Then, we propose a new procedure to build these unsupervised trees efficiently. The performance of this new metric is illustrated on image clustering and neuron classification. Results show that the tree-based method outperforms standard methods based on standard bag-of-features procedures.