关键词: 显著性检测, 轮廓封闭性, 多尺度融合, 外观显著性, 互补性

Abstract: 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.

Key words: saliency detection, contour closure, multi-layer fusion, appearance saliency, complementary