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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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Published in:IET image processing 2017-11, Vol.11 (11), p.1103-1113
Main Authors: Rabbani, Navid, Nazari, Behzad, Sadri, Saeid, Rikhtehgaran, Reyhaneh
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Language:English
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description 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.
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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
title Efficient Bayesian approach to saliency detection based on Dirichlet process mixture
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