Considering　the　problem　of　discrete　texture　synthesis　and　the　time　for　texturing,　this　paper　proposes　a　novel　framework　for　synthesizing　texture　images　based　on　discrete　example-based　elements.　We　start　with　extracting　texture　feature　distribution　from　exemplars　and　then　produce　discrete　elements　based　on　the　cluster　algorithm.　After　initializing　a　texture　image,　we　propose　a　texture　optimization　algorithm　based　on　heuristic　searching　to　improve　the　quality　of　the　texture　image.　Final,　we　use　a　texture　transfer　method　based　on　Convolutional　Neural　Network　(CNN)　to　stylize　the　optimized　texture　image.　Our　results　show　that　the　proposed　texture　synthesis　method　can　significantly　improve　the　quality　of　discrete　texture　synthesis　and　effectively　shorten　the　time　for　texture　generation.