Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85331
Title: Learning a cross-modal hashing network for multimedia search
Authors: Tan, Yap Peng
Liong, Venice Erin
Lu, Jiwen
Keywords: Hashing
Cross-modal Retrieval
Issue Date: 2017
Source: Liong, V. E., Lu, J., & Tan, Y.-P. (2017, September). Learning a cross-modal hashing network for multimedia search. Paper presented at 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China (pp. 3700-3704). IEEE.
Abstract: In this paper, we propose a cross-modal hashing network (CMHN) method to learn compact binary codes for cross-modality multimedia search. Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep neural network to learn multiple pairs of hierarchical non-linear transformations, under which the nonlinear characteristics of samples can be well exploited and the modality gap is well reduced. Our model is trained under an iterative optimization procedure which learns a (1) unified binary code discretely and discriminatively through a classification-based hinge-loss criterion, and (2) cross-modal hashing network, one deep network for each modality, through minimizing the quantization loss between real-valued neural code and binary code, and maximizing the variance of the learned neural codes. Experimental results on two benchmark datasets show the efficacy of the proposed approach.
URI: https://hdl.handle.net/10356/85331
http://hdl.handle.net/10220/44604
DOI: 10.1109/ICIP.2017.8296973
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICIP.2017.8296973].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
IGS Conference Papers

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