It　is　widely　acknowledged　that　object　segmentation　is　a　significant　research　field　for　computer　vision　and　a　key　process　for　many　other　visual　tasks.　In　the　past　unsupervised　single-image　segmentation,　there　are　often　cases　where　the　segmentation　result　is　not　good.　In　the　current　supervised　single-image　segmentation,　it　is　necessary　to　rely　on　a　large　number　of　data　annotations　and　long-term　training　of　the　model.　Then,　people　attempted　to　segment　simultaneously　the　common　regions　from　multiple　images.　On　the　one　hand,　it　does　not　need　to　use　a　large　amount　of　labeled　data　to　train　in　advance.　On　the　other　hand,　it　utilizes　the　consistency　constraint　between　images　to　better　obtain　the　object　information.　This　idea　can　generate　better　performance　than　the　traditional　one　did,　resulting　in　many　methods　related　to　object　co-segmentation.　This　paper　reviews　some　classic　and　effective　object　co-segmentation　methods,　including　saliency-based　approaches,　joint-processing-based　approaches,　graph-based　approaches,　and　others.　For　different　methods,　we　select　two　or　three　related　models　to　elaborate,　such　as　a　model　based　on　random　walks.　Moreover,　in　order　to　exhibit　and　evaluate　these　methods　objectively　and　comprehensively,　we　not　only　summarize　them　in　the　form　of　flowcharts　and　algorithm　summaries,　but　also　compare　their　performance　with　visualization　methods　and　evaluation　metrics,　such　as　intersection-over-union,　consistency　error,　and　precision-recall　rate.　From　the　experiment,　we　also　attempt　to　clarify　and　analyze　the　existing　problems.　Finally,　we　point　out　the　challenges　and　directions　and　open　new　venues　for　future　researchers　in　thefield.