{
  "id": "1806.09170",
  "version": "v1",
  "published": "2018-06-24T15:57:19.000Z",
  "updated": "2018-06-24T15:57:19.000Z",
  "title": "Fusion of complex networks and randomized neural networks for texture analysis",
  "authors": [
    "Lucas C. Ribas",
    "Jarbas J. M. Sa Junior",
    "Leonardo F. S. Scabini",
    "Odemir M. Bruno"
  ],
  "comment": "13 pages, 4 figures",
  "categories": [
    "cs.CV",
    "cs.LG",
    "physics.data-an"
  ],
  "abstract": "This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex networks and its topological properties as well as the image pixels are used to train randomized neural networks in order to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracies of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-06-24T15:57:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "complex networks",
      "high discriminative texture analysis method",
      "train randomized neural networks",
      "texture analysis field",
      "image pixels"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 13,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}