Recent　advances　in　the　corporate　processing　of　images　have　exhibited　its　advantages　over　the　individual　processing.　Image　co-segmentation　aims　to　segment　shared　objects　from　two　or　more　relevant　images,　and　is　becoming　more　and　more　interesting　for　computer　vision　researchers.　Many　applications　need　accurate　and　efficient　segmentation　techniques:　indoor　navigation,　autonomous　driving,　and　virtual　reality　systems　to　name　a　few.　Although　numerous　techniques　have　been　proposed,　it　is　still　lacking　a　deep　review　of　image　co-segmentation　techniques.　In　this　paper,　we　provide　a　review　on　the　fundamentals　and　challenges　of　image　co-segmentation　techniques.　We　organize　the　recent　advances　of　image　co-segmentation　into　seven　major　frameworks:　graph　based　framework,　clustering　based　framework,　partial　differential　equation　based　framework,　quadratic　programming　based　framework,　low　rank　matrix　recovery　based　framework,　joint　optimization　based　framework,　and　machine　learning　based　framework.　We　expect　this　review　to　be　beneficial　to　both　fresh　and　senior　researchers　in　this　field.