Efficient saliency analysis based on wavelet transform and entropy theory

Xiaolong Ma, Xudong Xie, Kin Man Lam, Yisheng Zhong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


Saliency detection has extensive applications in daily life. In this paper, an efficient saliency-detection method based on wavelet transform and entropy theory is proposed. In the algorithm proposed in this paper, salient regions are viewed as uncommon regions in the background of an image. The uncommon regions can be caused by differences in color, orientation, texture, shape, or other factors. Considering the fact that wavelet coefficients can represent the local features of an image in different scales and orientations, the wavelet transform is therefore employed to identify the salient regions. Unlike those conventional wavelet-based methods, our proposed method need not perform the inverse wavelet transformation; this can reduce the computational requirements. In addition, because the different factors (i.e. color, orientation, texture, shape, etc.) stimulate different aspects of the human visual system, a saliency-map combination scheme based on the entropy theory is devised in this paper, which can evaluate the influence or significance of the different factors. Experimental results show that our method, based on wavelet transformation and entropy theory, can achieve excellent performance in terms of the area under the receiver operating characteristic curve (AUC) score, the linear correlation coefficient (CC), the normalized scan-path saliency (NSS) score, and visual performance, as compared to existing state-of-the-art methods.
Original languageEnglish
Pages (from-to)201-207
Number of pages7
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 1 Jan 2015


  • Bottom-up approach
  • Color information
  • Entropy theory
  • Fixation-prediction
  • Human vision system
  • Preprocessing technique
  • Saliency detection
  • Wavelet transform

ASJC Scopus subject areas

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


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