Image　co-segmentation　and　co-localization　exploit　inter-image　information　to　identify　and　extract　foreground　objects　with　a　batch　mode.　However,　they　remain　challenging　when　confronted　with　large　object　variations　or　complex　backgrounds.　This　paper　proposes　a　multi-view　graph　embedding　(MV-Gem)　learning　scheme　which　integrates　diversity,　robustness　and　discernibility　of　object　features　to　alleviate　this　phenomenon.　To　encourage　the　diversity,　the　deep　co-information　containing　both　low-layer　general　representations　and　high-layer　semantic　information　is　generated　to　form　a　multi-view　feature　pool　for　comprehensive　co-object　description.　To　enhance　the　robustness,　a　multi-view　adaptive　weighted　learning　is　formulated　to　fuse　the　deep　co-information　for　feature　complementation.　To　ensure　the　discernibility,　the　graph　embedding　and　sparse　constraint　are　embedded　into　the　fusion　formulation　for　feature　selection.　The　former　aims　to　inherit　important　structures　from　multiple　views,　and　the　latter　further　selects　important　features　to　restrain　irrelevant　backgrounds.　With　these　techniques,　MV-Gem　gradually　recovers　all　co-objects　through　optimization　iterations.　Extensive　experimental　results　on　real-world　datasets　demonstrate　that　MV-Gem　is　capable　of　locating　and　delineating　co-objects　in　an　image　group.