Integrating　low-level　edge　features　has　been　proven　to　be　effective　in　preserving　clear　boundaries　of　salient　objects.　However,　the　locality　of　edge　features　makes　it　difficult　to　capture　globally　salient　edges,　leading　to　distraction　in　the　final　predictions.　To　address　this　problem,　we　propose　to　produce　distraction-free　edge　features　by　incorporating　cross-scale　holistic　interdependencies　between　high-level　features.　In　particular,　we　first　formulate　our　edge　features　extraction　process　as　a　boundary-filling　problem.　In　this　way,　we　enforce　edge　features　to　focus　on　closed　boundaries　instead　of　those　disconnected　background　edges.　Second,　we　propose　to　explore　cross-scale　holistic　contextual　connections　between　every　position　pair　of　high-level　feature　maps　regardless　of　their　distances　across　different　scales.　It　selectively　aggregates　features　at　each　position　based　on　its　connections　to　all　the　others,　simulating　the　＂contrast＂　stimulus　of　visual　saliency.　Finally,　we　present　a　complementary　features　integration　module　to　fuse　low-　and　high-level　features　according　to　their　properties.　Experimental　results　demonstrate　our　proposed　method　outperforms　previous　state-of-the-art　methods　on　the　benchmark　datasets,　with　the　fast　inference　speed　of　30　FPS　on　a　single　GPU.