Visual Saliency Detection Via Background Features and Object-Location Cues
Muwei Jian, Jing Wang, Hui Ling Yu · 2019
In this work, we describe a simple visual saliency-detection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are selected to represent directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to create a credible saliency map. Simulation experiments verify that the designed algorithm is useful and effective.