Efficient Bayesian approach to saliency detection based on Dirichlet process mixture. Issue 11 (25th September 2017)

• Record Type: Journal Article
• Authors: Rabbani, Navid; Nazari, Behzad; Sadri, Saeid; Rikhtehgaran, Reyhaneh
• Is Part Of: IET image processing. Volume 11:Issue 11(2017)
• Page Start: 1103
• Page End: 1113
• Publication Date: 2017-09-25
• Subjects: Bayes methods -- image processing -- statistical analysis; efficient Bayesian approach -- saliency detection -- image processing application -- image saliency -- location‐based saliency -- feature‐based saliency -- centre‐surround saliency -- statistical approach -- Dirichlet process mixture model; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0267
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Saliency detection has shown a great role in many image processing applications. This study introduces a new Bayesian framework for saliency detection. In this framework, image saliency is computed as product of three saliencies: location‐based, feature‐based and centre‐surround saliencies. Each of these saliencies is estimated using statistical approaches. The centre‐surround saliency is estimated using Dirichlet process mixture model. The authors evaluate their method using five different databases and it is shown that it outperform state‐of‐the‐art methods. Also, they show that the proposed method has a low computational cost.