深度共性保持哈希

doi:10.19665/j.issn1001-2400.2021.03.009

Abstract

Abstract:

At present,most existing cross-modal hashing methods fail to explore the relevance and diversity of different modality data,thus leading to unsatisfactory search performance.In order to solve the above problem,a simple yet efficient deep hashing model is proposed,named deep consistency-preserving hashing for cross-modal retrieval that simultaneously exploits modality-common representation and modality-private representation through the simple end-to-end network structure,and generates compact and discriminative hash codes for multiple modalities.Compared with other deep cross-modal hashing methods,the complexity and computation of the proposed method can be neglected with significant performance improvements.Comprehensive evaluations are conducted on three cross-modal benchmark datasets which illustrate that the proposed method is superior to the state-of-the-art cross-modal hashing methods.

Key words: multi-modal learning, hash, modality-common representation, modality-private representation, retrieval

CLC Number:

TP391

SHI Juan,XIE De,JIANG Qing. Deep consistency-preserving hashing[J].Journal of Xidian University, 2021, 48(3): 71-77.

Figures/Tables 5

References 18

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任务	方法	MIRFLICKR-25K			IAPRTC-12			NUS-WIDE
任务	方法	16 bit	32 bit	64 bit	16 bit	32 bit	64 bit	16 bit	32 bit	64 bit
I→T	CVH^[14]	0.587	0.586	0.581	0.444	0.465	0.47	0.508	0.508	0.508
	STMH^[15]	0.721	0.729	0.774	0.463	0.540	0.578	0.689	0.763	0.809
	CMSSH^[16]	0.64	0.645	0.645	0.509	0.462	0.492	0.671	0.698	0.578
	SCM^[17]	0.778	0.791	0.797	0.651	0.667	0.677	0.645	0.677	0.691
	LSSH^[18]	0.647	0.714	0.713	0.562	0.592	0.603	0.611	0.647	0.741
	DCMH^[9]	0.834	0.848	0.855	0.662	0.682	0.721	0.567	0.606	0.646
	文中方法	0.903	0.900	0.907	0.857	0.839	0.871	0.752	0.821	0.847
T→I	CVH^[14]	0.504	0.517	0.525	0.362	0.285	0.299	0.261	0.256	0.252
	STMH^[15]	0.671	0.697	0.725	0.459	0.510	0.573	0.603	0.679	0.702
	CMSSH^[16]	0.548	0.486	0.504	0.379	0.411	0.375	0.541	0.448	0.469
	SCM^[17]	0.812	0.818	0.819	0.720	0.744	0.754	0.707	0.740	0.755
	LSSH^[18]	0.654	0.691	0.719	0.502	0.553	0.583	0.554	0.628	0.691
	DCMH^[9]	0.840	0.860	0.861	0.717	0.692	0.78	0.662	0.672	0.700
	文中方法	0.874	0.880	0.891	0.845	0.844	0.873	0.769	0.832	0.852

任务	方法	比特数
任务	方法	16 bit	32 bit	64 bit
I→T	CMDVH^[8]	0.720	0.773	0.800
	SSAH^[11]	0.790	0.797	0.817
	文中方法	0.842	0.864	0.878
T→I	CMDVH^[8]	0.735	0.774	0.804
	SSAH^[11]	0.786	0.819	0.808
	文中方法	0.813	0.847	0.852

Deep consistency-preserving hashing

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References 18

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