The　combination　of　local　features　with　sparse　technique　has　improved　image　classification　performance　dramatically　in　recent　years.　Although　very　effective,　this　strategy　still　has　two　shortcomings.　First,　local　features　are　often　extracted　in　a　pre-defined　way　(e.g.　SIFT　with　dense　sampling)　without　considering　the　classification　task.　Second,　the　codebook　is　generated　by　sparse　coding　or　its　variants　by　minimizing　the　reconstruction　error　which　has　no　direct　relationships　with　the　classification　process.　To　alleviate　the　two　problems,　we　propose　a　novel　boosted　local　features　method　with　random　orientation　and　location　selection.　We　first　extract　local　features　with　random　orientation　and　location　using　a　weighting　strategy.　This　randomization　process　makes　us　to　extract　more　types　of　information　for　image　representation　than　pre-defined　methods.　These　extracted　local　features　are　then　encoded　by　sparse　representation.　Instead　of　generating　the　codebook　in　a　single　process,　we　construct　a　series　of　codebooks　and　the　corresponding　encoding　parameters　of　local　features　using　a　boosting　strategy.　The　weights　of　local　features　are　determined　by　the　classification　performances　of　learned　classifiers.　In　this　way,　we　are　able　to　combine　the　local　feature　extraction　and　encoding　with　classifier　training　into　a　unified　framework　and　gradually　improve　the　image　classification　performance.　Experiments　on　several　public　image　datasets　prove　the　effectiveness　and　efficiency　of　the　proposed　method.　(C)　2015　Elsevier　Inc.　All　rights　reserved.