• DocumentCode
    589367
  • Title

    Combining Holistic and Object-Based Approaches for Scene Classification

  • Author

    Zenghai Chen ; Zheru Chi ; Hong Fu ; Dagan Feng

  • Author_Institution
    Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Hong Kong, China
  • Volume
    1
  • fYear
    2012
  • fDate
    28-29 Oct. 2012
  • Firstpage
    65
  • Lastpage
    68
  • Abstract
    There are two main approaches for scene classification: holistic and object-based. Holistic approach is good at representing scenes with simple content. However, since it does not take into account the internal object relationship, holistic approach does not well characterize complex scenes with multiple objects. by contrast, object-based approach estimates the scene class by analyzing the object co-occurrence information, as a result of which it is advantageous in characterizing scenes with complex content. but object-based approach is not good at classifying simple scenes. in this paper, we combine holistic and object-based approaches for scene classification. the proposed combinatory approach is able to take advantages of the two approaches. Several state-of-the-art holistic and object-based approaches are compared. the experiments conducted on a widely-used scene dataset demonstrate the superiors performance of the combinatory approach.
  • Keywords
    combinatorial mathematics; image classification; natural scenes; combinatory approach; complex scenes; holistic approaches; internal object relationship; object cooccurrence information; object-based approaches; scene classification; scene dataset; Accuracy; Computed tomography; Computer vision; Lakes; Rivers; Semantics; Visualization; CENTRIST; holistic approach; object-based approach; scene classification; spatial pyramid matching (SPM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4673-2646-9
  • Type

    conf

  • DOI
    10.1109/ISCID.2012.25
  • Filename
    6406876