{
  "id": "0804.1448",
  "version": "v1",
  "published": "2008-04-09T10:06:15.000Z",
  "updated": "2008-04-09T10:06:15.000Z",
  "title": "Fast k Nearest Neighbor Search using GPU",
  "authors": [
    "Vincent Garcia",
    "Eric Debreuve",
    "Michel Barlaud"
  ],
  "comment": "13 pages, 2figures, submitted to CVGPU 2008",
  "categories": [
    "cs.CV",
    "cs.DC"
  ],
  "abstract": "The recent improvements of graphics processing units (GPU) offer to the computer vision community a powerful processing platform. Indeed, a lot of highly-parallelizable computer vision problems can be significantly accelerated using GPU architecture. Among these algorithms, the k nearest neighbor search (KNN) is a well-known problem linked with many applications such as classification, estimation of statistical properties, etc. The main drawback of this task lies in its computation burden, as it grows polynomially with the data size. In this paper, we show that the use of the NVIDIA CUDA API accelerates the search for the KNN up to a factor of 120.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2008-04-09T10:06:15.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "nearest neighbor search",
      "nvidia cuda api accelerates",
      "computer vision community",
      "highly-parallelizable computer vision problems",
      "graphics processing units"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 13,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2008arXiv0804.1448G"
    }
  }
}