We　propose　an　image　classification　framework　by　leveraging　the　non-negative　sparse　coding,　correlation　constrained　low　rank　and　sparse　matrix　decomposition　technique　(CCLR-Sc+SPM).　First,　we　propose　a　new　non-negative　sparse　coding　along　with　max　pooling　and　spatial　pyramid　matching　method　(Sc+SPM)　to　extract　local　feature＇s　information　in　order　to　represent　images,　where　non-negative　sparse　coding　is　used　to　encode　local　features.　Max　pooling　along　with　spatial　pyramid　matching　(SPM)　is　then　utilized　to　get　the　feature　vectors　to　represent　images.　Second,　we　propose　to　leverage　the　correlation　constrained　low-rank　and　sparse　matrix　recovery　technique　to　decompose　the　feature　vectors　of　images　into　a　low-rank　matrix　and　a　sparse　error　matrix　by　considering　the　correlations　between　images.　To　incorporate　the　common　and　specific　attributes　into　the　image　representation,　we　still　adopt　the　idea　of　sparse　coding　to　recode　the　Sc+SPM　representation　of　each　image.　In　particular,　we　collect　the　columns　of　the　both　matrixes　as　the　bases　and　use　the　coding　parameters　as　the　updated　image　representation　by　learning　them　through　the　locality-constrained　linear　coding　(LLC).　Finally,　linear　SVM　classifier　is　trained　for　final　classification.　Experimental　results　show　that　the　proposed　method　achieves　or　outperforms　the　state-of-the-art　results　on　several　benchmarks.　(C)　2014　Elsevier　Inc.　All　rights　reserved.