Fast k Nearest Neighbor Search using GPU

Statistical measures coming from information theory represent interesting bases for image and video processing tasks such as image retrieval and video object tracking. For example, let us mention the entropy and the Kullback-Leibler divergence. Accurate estimation of these measures requires to adapt to the local sample density, especially if the data are high-dimensional. The k nearest neighbor (kNN) framework has been used to define efficient variable-bandwidth kernel-based estimators with such a locally adaptive property. Unfortunately, these estimators are computationally intensive since they rely on searching neighbors among large sets of d-dimensional vectors. This computational burden can be reduced by pre-structuring the data, e.g. using binary trees as proposed by the Approximated Nearest Neighbor (ANN) library. Yet, the recent opening of Graphics Processing Units (GPU) to generalpurpose computation by means of the NVIDIA CUDA API offers the image and video processing communit...

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Vincent Garcia, Eric Debreuve, Michel Barlaud
• Keywords: Brute Force, CORR 2008, Education, Variable-bandwidth Kernel-based Estimators, Video Processing Tasks