Inspired by Gestalt theory in psychology

we proposes a saliency detection method with multi-layer contour-region characteristic by using the complementary between contour cue and appearance cue

to address the problem that object is indiscoverable in the clutter scene

Firstly

after the image super-pixel segmentation

two kinds of appearance features are extracted respectively

including global appearance cue based on color histogram and local contrast cues based on the regional neighborhood relations. Secondly

in order to solve the object confusion caused by small difference in the appearance

we extract object closure feature to describe the saliency from object contour. Finally

we introduce multi-scale segmentation into our model to enhance the robustness for the diversity of object size

and use the support vector machine to optimize the fusion weights of contour-region cues. Experiments on ASD

MSRA10K

SED2 datasets show that

compared to other state-of-the-art methods

our model can improve the recall and precision measure due to the introduction of contour closure characteristic.