@Inproceedings{mikulik_sisap13,
  IS = { zkontrolovano 24 Jan 2014 },
  UPDATE  = { 2013-09-25 },
    author     = {Mikulik, Andrej and Chum, Ond{\v{r}}ej and Matas, Ji{\v{r}}{\'\i}},
    title      = {Image Retrieval for Online Browsing in Large Image Collections},
    booktitle  = {Similarity Search and Applications},
    year       = {2013},
    series     = {8199},
    pages      = {3--15},
    book_pages = {332},
    month      = {October},
    day        = {2-4},
    venue      = {A Coruna, Spain},
    publisher  = {Springer},
    annote     = {Two new methods for large scale image retrieval are proposed, showing
    that the classical ranking of images based on similarity addresses
    only one of possible user requirements. The novel retrieval methods
    add zoom-in and zoom-out capabilities and answer the 'What is this?'
    and 'Where is this?' questions.
    The functionality is obtained by modifying the scoring and ranking
    functions of a standard bag-of-words image retrieval pipeline. We
    show the importance of the DAAT scoring and query expansion for recall
    of zoomed images.
    The proposed methods were tested on a standard large annotated image
    dataset together with images of Sagrada Familia and 100000 image
    confusers downloaded from Flickr. For completeness, we present in
    detail components of image retrieval pipelines in state-of-the-art
    systems. Finally, open problems related to zoom-in and zoom-out queries
    are discussed.},
    doi        = {10.1007/978-3-642-41062-8_2},
    isbn       = {978-3-642-41061-1},
    issn       = {0302-9743},
    editor     = {Brisaboa, Nieves and Pedreira, Oscar and Zezula, Pavel},
    project    = {ERC-CZ LL1303, GACR P103/12/G084, Microsoft scholarship},
    authorship = {34-33-33},
    address    = {Heidelberg, Germany},
    keywords   = {image retrieval, indexing, query processing, zoom},
}