• DocumentCode
    56704
  • Title

    Colour combination attention for object recognition

  • Author

    Zhu, Junan ; Yu, Jinpeng ; Wang, Chingyue ; Li, Fan-Zhang

  • Author_Institution
    Beijing Key Lab. of Traffic Data Anal. & Min., Beijing Jiaotong Univ., Beijing, China
  • Volume
    8
  • Issue
    9
  • fYear
    2014
  • fDate
    Sep-14
  • Firstpage
    539
  • Lastpage
    547
  • Abstract
    Within the bag-of-words (BOWs) framework, the multiple cues fusion methods provide excellent results in object and scene classification. Top-down colour attention (CA) method is developed to use colour to guide attention by means of a top-down category-specific attention map. In this method, more features are taken from category-specific colour regions where objects are more likely to be contained. In CA, the colours on the object are considered separately, so the diversity of object colours and large intra-class colour variation make the discrimination of every colour on the object different. Object could be recognised as a collection of related colours. To enhance the object recognition capability of CA, our colour combination attention method uses mutual information and the colour combination histogram to estimate and combine the colours on the object. Results are presented on three challenging data sets, and the experiments demonstrate that the proposed feature fusion method significantly outperforms the state-of-the-art methods.
  • Keywords
    image classification; image colour analysis; image fusion; object recognition; BOW framework; bag-of-words framework; category-speciflc colour regions; colour combination attention; colour combination histogram; feature fusion method; intraclass colour variation; multiple cues fusion methods; object classiflcation; object colours diversity; object recognition; scene classiflcation; top-down CA method; top-down category-speciflc attention map; top-down colour attention method;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IET
  • Publisher
    iet
  • ISSN
    1751-9659
  • Type

    jour

  • DOI
    10.1049/iet-ipr.2013.0431
  • Filename
    6892146