Bayesian salient object detection based on saliency driven clustering

Lei Zhou, Keren Fu, Yijun Li, Yu Qiao, Xiangjian He, Jie Yang

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)


Salient object detection is essential for applications, such as image classification, object recognition and image retrieval. In this paper, we design a new approach to detect salient objects from an image by describing what does salient objects and backgrounds look like using statistic of the image. First, we introduce a saliency driven clustering method to reveal distinct visual patterns of images by generating image clusters. The Gaussian Mixture Model (GMM) is applied to represent the statistic of each cluster, which is used to compute the color spatial distribution. Second, three kinds of regional saliency measures, i.e, regional color contrast saliency, regional boundary prior saliency and regional color spatial distribution, are computed and combined. Then, a region selection strategy integrating color contrast prior, boundary prior and visual patterns information of images is presented. The pixels of an image are divided into either potential salient region or background region adaptively based on the combined regional saliency measures. Finally, a Bayesian framework is employed to compute the saliency value for each pixel taking the regional saliency values as priority. Our approach has been extensively evaluated on two popular image databases. Experimental results show that our approach can achieve considerable performance improvement in terms of commonly adopted performance measures in salient object detection.

Original languageEnglish
Pages (from-to)434-447
Number of pages14
JournalSignal Processing: Image Communication
Issue number3
Publication statusPublished - Mar 2014
Externally publishedYes


  • Bayesian model
  • Regional saliency computation
  • Saliency driven clustering
  • Saliency object detection

ASJC Scopus subject areas

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


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