Attention-driven image interpretation with application to image retrieval

Hong Fu, Zheru Chi, Dagan Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

77 Citations (Scopus)


Visual attention, a selective procedure of human's early vision, plays a very important role for humans to understand a scene by intuitively emphasizing some focused regions/objects. Being aware of this, we propose an attention-driven image interpretation method that pops out visual attentive objects from an image iteratively by maximizing a global attention function. In this method, an image can be interpreted as containing several perceptually attended objects as well as a background, where each object has an attention value. The attention values of attentive objectives are then mapped to importance factors so as to facilitate the subsequent image retrieval. An attention-driven matching algorithm is proposed in this paper based on a retrieval strategy emphasizing attended objects. Experiments on 7376 Hemera color images annotated by keywords show that the retrieval results from our attention-driven approach compare favorably with conventional methods, especially when the important objects are seriously concealed by the irrelevant background.
Original languageEnglish
Pages (from-to)1604-1621
Number of pages18
JournalPattern Recognition
Issue number9
Publication statusPublished - 1 Sep 2006

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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