{
  "id": "1610.02055",
  "version": "v1",
  "published": "2016-10-06T20:14:13.000Z",
  "updated": "2016-10-06T20:14:13.000Z",
  "title": "Places: An Image Database for Deep Scene Understanding",
  "authors": [
    "Bolei Zhou",
    "Aditya Khosla",
    "Agata Lapedriza",
    "Antonio Torralba",
    "Aude Oliva"
  ],
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "abstract": "The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world. Using state of the art Convolutional Neural Networks, we provide impressive baseline performances at scene classification. With its high-coverage and high-diversity of exemplars, the Places Database offers an ecosystem to guide future progress on currently intractable visual recognition problems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-10-06T20:14:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep scene understanding",
      "image database",
      "data-hungry machine learning algorithms",
      "art convolutional neural networks",
      "reach near-human semantic classification"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}