• DocumentCode
    3408865
  • Title

    Hierarchical object groups for scene classification

  • Author

    Sadovnik, Amir ; Tsuhan Chen

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
  • fYear
    2012
  • fDate
    Sept. 30 2012-Oct. 3 2012
  • Firstpage
    1881
  • Lastpage
    1884
  • Abstract
    The hierarchical structures that exist in natural scenes have been utilized for many tasks in computer vision. The basic idea is that instead of using strictly low level features it is possible to combine them into higher level hierarchical structures. These higher level structures provide a more specific feature and can thus lead to better results in classification or detection. Although most previous work has focused on hierarchical combinations of low level features, hierarchical structures exist on higher levels as well. In this work we attempt to automatically discover these higher level structures by finding meaningful object groups using the Minimum Description Length (MDL) principle. We then use these structures for scene classification and show that we can achieve a higher accuracy rate using them.
  • Keywords
    computer vision; image classification; MDL; computer vision; hierarchical object groups; hierarchical structures; minimum description length; scene classification; Accuracy; Detectors; Feature extraction; Object detection; Painting; Training; Vectors; Image Classification; Object Detection; Object Groups; Scene Classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2012 19th IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4673-2534-9
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2012.6467251
  • Filename
    6467251