An efficient algorithm for attention-driven image interpretation from segments

Hong FU, Zheru CHI, Dagan FENG

Research output: Contribution to journalArticlespeer-review

13 Citations (Scopus)

Abstract

In the attention-driven image interpretation process, an image is interpreted as containing several perceptually attended objects as well as the background. The process benefits greatly a content-based image retrieval task with attentively important objects identified and emphasized. An important issue to be addressed in an attention-driven image interpretation is to reconstruct several attentive objects iteratively from the segments of an image by maximizing a global attention function. The object reconstruction is a combinational optimization problem with a complexity of 2 which is computationally very expensive when the number of segments N is large. In this paper, we formulate the attention-driven image interpretation process by a matrix representation. An efficient algorithm based on the elementary transformation of matrix is proposed to reduce the computational complexity to 3ωN(N – 1)² /2, where ω is the number of runs. Experimental results on both the synthetic and real data show a significantly improved processing speed with an acceptable degradation to the accuracy of object formulation. Copyright © 2008 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)126-140
JournalPattern Recognition
Volume42
Issue number1
Early online date01 Jul 2008
DOIs
Publication statusPublished - Jan 2009

Citation

Fu, H., Chi, Z., & Feng, D. (2009). An efficient algorithm for attention-driven image interpretation from segments. Pattern Recognition, 42(1), 126-140. doi: 10.1016/j.patcog.2008.06.021

Keywords

  • Computer vision
  • Search optimization
  • Region combination
  • Visual attention model
  • Image understanding
  • Content-based image retrieval

Fingerprint Dive into the research topics of 'An efficient algorithm for attention-driven image interpretation from segments'. Together they form a unique fingerprint.