Inspired　by　the　photometric　invariance　of　color　space,　this　paper　proposes　a　simple　yet　powerful　descriptor　for　object　detection　and　recognition,　called　Rotative　Maximal　Pattern　(RMP).　The　effectiveness　of　RMP　comes　from　the　two　components:　Rotatable　Couple　Templates　(RCTs)　with　max　pooling,　and　Normalized　Histogram　Intersection　(NHI)　with　the　theoretical　guarantee.　More　concretely,　RCTs　are　the　combination　of　two　templates　to　code　the　possible　rotations.　NHI　serves　as　the　similarity　between　two　color　histograms.　We　have　conducted　extensive　experiments　on　INRIA　pedestrian　and　Pascal　VOC2007　data　sets　for　object　detection　tasks;　we　also　show　that　our　approach　leads　to　a　promising　performance　on　Caltech　101,　Scene　15,　UIUCsport　and　Stanford　40　action　data　sets.　(C)　2017　Published　by　Elsevier　Inc.