Human　matting,　high　quality　extraction　of　humans　from　natural　images,　is　crucial　for　a　wide　variety　of　applications　such　as　virtual　reality,　augmented　reality,　entertainment　and　so　on.　Since　the　matting　problem　is　an　ill-posed　problem,　most　previous　methods　rely　on　extra　user　inputs　such　as　trimap　or　scribbles　as　guidance　to　estimate　alpha　value　for　the　pixels　that　are　in　the　unknown　region　of　the　trimap.　This　phenomenon　makes　it　difficult　to　be　applied　to　large　scale　data.　In　order　to　solve　these　problems,　we　studied　the　unique　role　of　semantics　and　details　in　image　matting,　and　decomposed　the　matting　task　into　two　sub-tasks:　trimap　segmentation　based　on　high-level　semantic　information　and　alpha　regression　based　on　low-level　detailed　information.　Specifically,　we　proposed　a　novel　Cascaded　Segmented　Matting　Network　(CSMNet),　which　uses　a　shared　encoder　and　two　separate　decoders　to　learn　these　two　tasks　in　a　collaborative　way　to　achieve　the　end-to-end　human　image　matting.　In　addition,　we　established　a　large-scale　dataset　with　14,000　fine-labeled　human　matting　images.　A　background　dataset　is　also　built　to　simulate　real　pictures.　Comprehensive　empirical　studies　on　above　datasets　demonstrate　that　CSMNet　could　produce　a　stable　and　accurate　alpha　matte　without　the　input　of　trimap　and　achieve　an　evaluation　value　that　is　comparable　to　the　algorithm　that　requires　trimap.