The　objective　of　saliency　detection　is　to　extract　a　salient　map　in　a　natural　image.　However,　the　detection　of　salient　regions　located　by　the　object　is　challenging　due　to　some　factors　such　as　multi-object　overlapping,　salient　object　touching　the　image　boundaries,　and　salient　region　on　color,　texture,　and　shape.　To　alleviate　those　issues,　we　proposed　a　two-phase　strategy　saliency　model　for　saliency　detection　by　using　a　robust　color　channel　volume　and　background　likelihood　weight.　Specifically,　we　first　linearly　combine　several　color　space　channels　of　Lab　color　space　to　construct　a　low-level　image　features　extractor　called　color　channel　volume　which　is　brilliant　to　stand　out　a　salient　region　but　suppress　a　background　region.　Then,　based　on　super　pixel　segmentation,　we　design　a　robust　background　region　measure　called　background　likelihood　weight　(BLW)　to　find　the　background　region　from　the　color　channel　volume　and　obtain　an　initial　salient　map　by　removing　the　found　background　region.　Finally,　we　proposed　improved　multimodal　histogram　thresholding　to　refine　the　initial　salient　map　and　further　extract　a　final　salient　map.　Experimental　results　on　two　public　image　datasets　show　that　our　approach　achieves　better　performance　results　of　saliency　detection　than　several　other　saliency　detection　algorithms　in　metrics.