Salient　object　detection,　as　a　necessary　step　of　many　computer　vision　applications,　has　attracted　extensive　attention　in　recent　years.　A　novel　salient　object　detection　method　is　proposed　based　on　multi-superpixel-scale　contrast.　Saliency　value　of　each　superpixel　is　measured　with　a　global　score,　which　is　computed　using　the　region＇s　colour　contrast　and　the　spatial　distances　to　all　other　regions　in　the　image.　High-level　information　is　also　incorporated　to　improve　the　performance,　and　the　saliency　maps　are　fused　across　multiple　levels　to　yield　a　reliable　final　result　using　the　modified　multi-layer　cellular　automata.　The　proposed　algorithm　is　evaluated　and　compared　with　five　state-of-the-art　approaches　on　three　publicly　standard　datasets.　Both　quantitative　and　qualitative　experimental　results　demonstrate　the　effectiveness　and　efficiency　of　the　proposed　method.