Scalable Graph Hashing with Feature Transformation Experiment Top-k Precision @TINY-1M 0.8 0.7 0.8 0.6 0.6 0.5 0.5 ★=SGH ★-SGH a +-T0 0.4 0.4 -AGH -AGH ◆-DGH-可 ◆-DGH 0.3 0.3 DGH-R -DGH-R ◆-PCAH PCAH ◆LSH 0.2 ◆-L3H 02 200 400 600 800 1000 200 400 600 800 1000 #Returned Samples #Retumed Samples (a)64 bit (b)128 bit 口卡+得二4元互)Q0 Li (http://cs.nju.edu.cn/lvj) Learning to Hash LAMDA.CS.NJU 34 /43Scalable Graph Hashing with Feature Transformation Experiment Top-k Precision @TINY-1M 200 400 600 800 1000 0.2 0.3 0.4 0.5 0.6 0.7 0.8 #Returned Samples Precision SGH ITQ AGH DGH−I DGH−R PCAH LSH (a) 64 bit 200 400 600 800 1000 0.2 0.3 0.4 0.5 0.6 0.7 0.8 #Returned Samples Precision SGH ITQ AGH DGH−I DGH−R PCAH LSH (b) 128 bit Li (http://cs.nju.edu.cn/lwj) Learning to Hash LAMDA, CS, NJU 34 / 43