The　task　of　person　re-identification　(re-id)　is　to　find　the　same　pedestrian　across　non-overlapping　cameras.　Normally,　the　performance　of　person　re-id　can　be　affected　by　background　clutters.　However,　existing　segmentation　algorithms　are　hard　to　obtain　perfect　foreground　person　images.　To　effectively　leverage　the　body　(foreground)　cue,　and　in　the　meantime　pay　attention　to　discriminative　information　in　the　background　(e.g.,　companion　or　vehicle),　we　propose　to　use　a　cross-learning　strategy　to　take　both　foreground　and　other　discriminative　information　into　account.　In　addition,　since　currently　existing　foreground　segmentation　result　always　involves　noise,　we　use　Label　Smoothing　Regularization　(LSR)　to　strengthen　the　generalization　capability　during　our　learning　process.　In　experiments,　we　pick　up　two　state-of-The-Art　person　re-id　methods　to　verify　the　effectiveness　of　our　proposed　cross-learning　strategy.　Our　experiments　are　carried　out　on　two　publicly　available　person　re-id　datasets.　Obvious　performance　improvements　can　be　observed　on　both　datasets.　©　2019　IEEE.