Robust seed selection of foreground and background priors based on directional blocks for saliency-detection system

Visual perception modelling of saliency detection has received widespread concerns recently from both the cybernetics and computational intelligence domains. In particular, those distinct background and foreground-oriented models are capable of engendering competitive results. The implicitly vital issue of the above computing approaches is how to reliably choose seeds of the foreground and background cues for kicking off the subsequent saliency-detection procedure. To address this barrier, this paper explores the local geometry and statistical attribute of the detected orientational blocks via an improved discrete wavelet frame transform algorithm to estimate the center position of individual salient object in the original input. Specially, the calculated centroid can be regarded as the prominent focus of visual perception in the initial image, which is beneficial to choose the credible seed during the computing of the superpixel-based foreground and background cues. Then, both sides of the complementary and visually oriented cues are integrated concurrently into a dependable and robust saliency map with reliability. Substantial experimental evaluations in term of freely open-access databases testify the effectiveness of the designed framework, and have prove that the designed algorithm is accurate and outperforms the other distinct representative saliency detection algorithms.