Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks

Table 5

MAP results for different numbers of bits (12, 24, 32, and 48 bits) on the two benchmark image datasets (CIFAR-10 and NUS-WIDE).

Methods		CIFAR-10				NUS-WIDE
		12 bits	24 bits	32 bits	48 bits	12 bits	24 bits	32 bits	48 bits
Deep methods	Ours	0.770	0.774	0.788	0.783	0.791	0.828	0.832	0.836
	DTSH	0.710	0.750	0.765	0.774	0.773	0.808	0.812	0.824
	DPSH	0.713	0.727	0.744	0.757	0.752	0.790	0.794	0.812
	DQN	0.554	0.558	0.564	0.580	0.768	0.776	0.783	0.792
	DHN	0.555	0.594	0.603	0.621	0.708	0.735	0.748	0.758
	NINH	0.552	0.566	0.558	0.581	0.674	0.697	0.713	0.715
	CNNH	0.439	0.511	0.509	0.522	0.611	0.618	0.625	0.608
Nondeep methods	FastH	0.305	0.349	0.369	0.384	0.621	0.650	0.665	0.687
	SDH	0.285	0.329	0.341	0.356	0.568	0.600	0.608	0.637
	KSH	0.303	0.337	0.346	0.356	0.556	0.572	0.581	0.588
	LFH	0.176	0.231	0.211	0.253	0.571	0.568	0.568	0.585
	SPLH	0.171	0.173	0.178	0.184	0.568	0.589	0.597	0.601
	ITQ	0.162	0.169	0.172	0.175	0.452	0.468	0.472	0.477
	SH	0.127	0.128	0.126	0.129	0.454	0.406	0.405	0.400