Grey Wolf Optimization on Modeling Saliency Detection in Stereoscopic 3D Images

Abstract

Till now, several saliency detection models have been introduced for numerous applications in multimedia developments. Anyhow, specific appliances of stereoscopic imaging require improvements in saliency detection schemes for extracting the salient regions in a precise manner. The saliency detection (SD) model faces numerous shortcomings like intricacy in natural images and minor-scale patterns on salient objects. Hence, this paper endeavors to attain the SD model in two levels; Feature extraction (FE), for which Gaussian kernel model is utilized to extort the features and depth SD, for which Gabor Filter (GF) is exploited to attain the depth of saliency map. Accordingly, the adopted scheme optimizes ‘2’ coefficients such as, feature difference betwixt image patches H in feature evaluation and also fine scale c from which the accurate detection is attained. For optimization purpose, a well-known optimization termed Grey Wolf Optimization (GWO) is exploited and evaluated for varying values of a , and the results are attained with respect to ROC (i.e. Receiver Operating Characteristic), and statistical analysis.