{
  "id": "1704.03946",
  "version": "v1",
  "published": "2017-04-12T22:20:06.000Z",
  "updated": "2017-04-12T22:20:06.000Z",
  "title": "Asymmetric Feature Maps with Application to Sketch Based Retrieval",
  "authors": [
    "Giorgos Tolias",
    "Ondřej Chum"
  ],
  "comment": "CVPR 2017",
  "categories": [
    "cs.CV"
  ],
  "abstract": "We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-04-12T22:20:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "asymmetric feature maps",
      "application",
      "translation invariant sketch-based image retrieval",
      "trigonometric polynomial",
      "short vector image representation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}