Efficient Bayesian approach to saliency detection based on Dirichlet process mixture

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...

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Bibliographic Details
Published in:IET image processing 2017-11, Vol.11 (11), p.1103-1113
Main Authors: Rabbani, Navid, Nazari, Behzad, Sadri, Saeid, Rikhtehgaran, Reyhaneh
Format: Article
Language:English
Subjects:
Bayes methods
centre‐surround saliency
Dirichlet process mixture model
efficient Bayesian approach
feature‐based saliency
image processing
image processing application
image saliency
location‐based saliency
Research Article
saliency detection
statistical analysis
statistical approach
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Summary: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.
ISSN:1751-9659
1751-9667
1751-9667
DOI:10.1049/iet-ipr.2017.0267