Deep Binary Representation for Efficient Image Retrieval

Table 3

MAP of image retrieval on MNIST, CIFAR10, and ImageNet dataset with 4 different bit lengths. We use 1000 query images and calculate the MAP within top 5000 returned neighbors in MNIST and CIFAR10 dataset. We use images from 100 random categories in ImageNet dataset and all validation images of these categories are used as query sets.

Method	MNIST (MAP)				CIFAR-10 (MAP)				ImageNet (MAP)
	12 bits	24 bits	32 bits	48 bits	12 bits	24 bits	32 bits	48 bits	16 bits	32 bits	48 bits	64 bits
DBR-v3	—	—	—	—	0.826	0.837	0.842	0.847	0.733	0.761	0.768	0.769
DBR	0.980	0.984	0.984	0.990	0.612	0.648	0.658	0.680	—	—	—	—
HashNet	—	—	—	—	—	—	—	—	0.442	0.606	0.663	0.684
DHN	—	—	—	—	0.555	0.594	0.603	0.621	0.311	0.472	0.542	0.573
DNNH	—	—	—	—	0.552	0.566	0.558	0.581	0.290	0.461	0.530	0.565
CNNH+	0.969	0.975	0.971	0.975	0.465	0.521	0.521	0.532	—	—	—	—
CNNH	0.957	0.963	0.956	0.960	0.439	0.511	0.509	0.522	0.281	0.450	0.525	0.554
KSH	0.872	0.891	0.897	0.900	0.303	0.337	0.346	0.356	0.160	0.298	0.342	0.394
ITQ-CCA	0.659	0.694	0.714	0.726	0.264	0.282	0.288	0.295	0.266	0.436	0.548	0.576
MLH	0.472	0.666	0.652	0.654	0.182	0.195	0.207	0.211	—	—	—	—
BRE	0.515	0.593	0.613	0.634	0.159	0.181	0.193	0.196	0.063	0.253	0.330	0.358
SH	0.265	0.267	0.259	0.250	0.131	0.135	0.133	0.130	0.207	0.328	0.395	0.419
ITQ	0.388	0.436	0.422	0.429	0.162	0.169	0.172	0.175	0.326	0.462	0.517	0.552
LSH	0.187	0.209	0.235	0.243	0.121	0.126	0.120	0.120	0.101	0.235	0.312	0.360