Salient object detection based on global to local visual search guidance

Abstract

The human eye has the behavior of fixation shift, which is represented by visual search mechanism. Human visual search mechanism has the characteristics of visual guidance from global to local. In order to imitate this behavior, this paper proposes a novel salient object detection model which integrates global and local perception based on visual search guidance. First, by using multi-level feature complementarity, a coarse global perceptual map can be obtained by encoding VGG network and decoding deconvolution network. Then, through the Maximally Stable External Regions (MSER) algorithm, the local hotspot sub-regions in the coarse global perceptual map are intercepted, and generating a visual guidance search path based on the maximum response principle, and the local fine perceptual map is successively merged through the Squeeze-and-Excitation Networks (SENet) step by step. Finally, the salient map integrating the coarse global and fine local perception is achieved. Because only one local area needs to be integrated at a time, which is equivalent to solving a relatively simple problem at a time, it is easy to get more accurate results. The experimental results show that the time-consuming of our model is relatively low and compared with the classical and more recent SOTA methods, our model also shows competitive results.