Mean shift based clustering in high dimensions: a texture classification example
- 1 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 456-463 vol.1
- https://doi.org/10.1109/iccv.2003.1238382
Abstract
Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are constrained to be spherically symmetric and their number has to be known a priori. In nonparametric clustering methods, like the one based on mean shift, these limitations are eliminated but the amount of computation becomes prohibitively large as the dimension of the space increases. We exploit a recently proposed approximation technique, locality-sensitive hashing (LSH), to reduce the computational complexity of adaptive mean shift. In our implementation of LSH the optimal parameters of the data structure are determined by a pilot learning procedure, and the partitions are data driven. As an application, the performance of mode and k-means based textons are compared in a texture classification study.Keywords
This publication has 14 references indexed in Scilit:
- Compact representation of bidirectional texture functionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Texture classification: are filter banks necessary?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Fast pose estimation with parameter-sensitive hashingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Polynomial time approximation schemes for geometric k-clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Mean shift: a robust approach toward feature space analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Constructing models for content-based image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2001
- Representing and Recognizing the Visual Appearance of Materials using Three-dimensional TextonsInternational Journal of Computer Vision, 2001
- Rotation-invariant texture classification using a complete space-frequency modelIEEE Transactions on Image Processing, 1999
- Approximate nearest neighborsPublished by Association for Computing Machinery (ACM) ,1998
- A simple algorithm for nearest neighbor search in high dimensionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997