Efficient Bayesian approach to saliency detection based on Dirichlet process mixture
Navid Rabbani, Behzad Nazari, Saeid Sadri, Reyhaneh Rikhtehgaran · IET Image Processing · 2017
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.