For　sparse　coding,　the　weaker　the　correlation　of　dictionary　atoms　is,　the　better　the　representation　capacity　of　dictionary　will　be.　A　weak　correlation　dictionary　construction　method　for　sparse　coding　has　been　proposed　in　this　paper.　Firstly,　a　new　dictionary　atom　initialization　is　proposed　in　which　data　samples　with　weak　correlation　are　selected　as　the　initial　dictionary　atoms　in　order　to　effectively　reduce　the　correlation　among　them.　Then,　in　the　process　of　dictionary　learning,　the　correlation　between　atoms　has　been　measured　by　correlation　coefficient,　and　strong　correlation　atoms　have　been　eliminated　and　replaced　by　weak　correlation　atoms　in　order　to　improve　the　representation　capacity　of　the　dictionary.　An　image　classification　scheme　has　been　achieved　by　applying　the　weak　correlation　dictionary　construction　method　proposed　in　this　paper.　Experimental　results　show　that,　the　proposed　method　averagely　improves　image　classification　accuracy　by　more　than　2%,　compared　to　sparse　coding　spatial　pyramid　matching　(ScSPM)　and　other　existing　methods　for　image　classification　on　the　datasets　of　Caltech-101,　Scene-15,　etc.　©　2017,　Shanghai　Jiaotong　University　and　Springer-Verlag　Berlin　Heidelberg.