access icon free 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 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.

Inspec keywords: statistical analysis; Bayes methods; image processing

Other keywords: image processing application; feature-based saliency; image saliency; saliency detection; centre-surround saliency; location-based saliency; efficient Bayesian approach; Dirichlet process mixture model; statistical approach

Subjects: Other topics in statistics; Optical, image and video signal processing; Other topics in statistics; Computer vision and image processing techniques

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