Image　classification　is　an　important　task　in　the　field　of　machine　learning　and　image　processing.　However,　common　classification　method,　the　K-Nearest-Neighbor　algorithm,　has　high　complexity,　because　its　two　main　processes:　similarity　computing　and　searching,　are　time-consuming.　Especially　in　the　era　of　big　data,　the　problem　is　prominent　when　the　amount　of　images　to　be　classified　is　large.　In　this　paper,　we　try　to　use　the　powerful　parallel　computing　ability　of　quantum　computers　to　optimize　the　efficiency　of　image　classification.　The　scheme　is　based　on　quantum　K-Nearest-Neighbor　algorithm.　Firstly,　the　feature　vectors　of　images　are　extracted　on　classical　computers.　Then,　the　feature　vectors　are　inputted　into　a　quantum　superposition　state,　which　is　used　to　achieve　parallel　computing　of　similarity.　Next,　the　quantum　minimum　search　algorithm　is　used　to　speed　up　searching　process　for　similarity.　Finally,　the　image　is　classified　by　quantum　measurement.　The　complexity　of　the　quantum　algorithm　is　only　O(root　kM),　which　is　superior　to　the　classical　algorithms.　Moreover,　the　measurement　step　is　executed　only　once　to　ensure　the　validity　of　the　scheme.　The　experimental　results　show　that　the　classification　accuracy　is　on　Graz-01　dataset　and　on　Caltech-101　dataset,　which　is　close　to　existing　classical　algorithms.　Hence,　our　quantum　scheme　has　a　good　classification　performance　while　greatly　improving　the　efficiency.