Semi-paired Multi-modal Query Hashing Method

YU Jun; MA Jiangtao; XIAN Yang; HOU Ruixia; SUN Wei

doi:10.11999/JEIT231072

Volume 46 Issue 2

Feb. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 481-491

YU Jun, MA Jiangtao, XIAN Yang, HOU Ruixia, SUN Wei. Semi-paired Multi-modal Query Hashing Method[J]. Journal of Electronics & Information Technology, 2024, 46(2): 481-491. doi: 10.11999/JEIT231072

Citation:

YU Jun, MA Jiangtao, XIAN Yang, HOU Ruixia, SUN Wei. Semi-paired Multi-modal Query Hashing Method[J]. Journal of Electronics & Information Technology, 2024, 46(2): 481-491. doi: 10.11999/JEIT231072

YU Jun, MA Jiangtao, XIAN Yang, HOU Ruixia, SUN Wei. Semi-paired Multi-modal Query Hashing Method[J]. Journal of Electronics & Information Technology, 2024, 46(2): 481-491. doi: 10.11999/JEIT231072

Citation:

YU Jun, MA Jiangtao, XIAN Yang, HOU Ruixia, SUN Wei. Semi-paired Multi-modal Query Hashing Method[J]. Journal of Electronics & Information Technology, 2024, 46(2): 481-491. doi: 10.11999/JEIT231072

PDF( 4991 KB)

Semi-paired Multi-modal Query Hashing Method

doi: 10.11999/JEIT231072

1.
College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
2.
Research Institute of Resource Information Techniques, CAF, Beijing, 100091, China
3.
Agricultural Information Institute of CAAS, Beijing 100081, China

Funds: The National Natural Science Foundation of China (32271880), The Science and Technology Research Project of Henan Provincial Department (222102210064), The Natural Science Foundation of Henan Province Science (232300420150)

Received Date: 2023-10-08
Rev Recd Date: 2024-01-31

Available Online: 2024-01-31

Publish Date: 2024-02-10

Abstract

Abstract

Multimodal hashing can convert heterogeneous multimodal data into unified binary codes. Due to its advantages of low storage cost and fast Hamming distance sorting, it has attracted widespread attention in large-scale multimedia retrieval. Existing multimodal hashing methods assume that all query data possess complete multimodal information to generate their joint hash codes. However, in practical applications, it is difficult to obtain fully complete multimodal information. To address the problem of missing modal information in semi-paired query scenarios, a novel Semi-paired Query Hashing (SPQH) method is proposed to solve the joint encoding problem of semi-paired query samples. Firstly, the proposed method performs projection learning and cross-modal reconstruction learning to maintain semantic consistency among multimodal data. Then, the semantic similarity structure information of the label space and complementary information among multimodal data are effectively captured to learn a discriminative hash function. During the query encoding stage, the missing modal features of unpaired sample data are completed using the learned cross-modal reconstruction matrix, and then the hash features are generated using the learned joint hash function. Compared to state-of-the-art baseline methods, the average retrieval accuracy on the Pascal Sentence, NUS-WIDE, and IAPR TC-12 datasets has improved by 2.48%. Experimental results demonstrate that the algorithm can effectively encode semi-paired multimodal query data and achieve superior retrieval performance.
- Multimodal retrieval,
- Hashing,
- Semi-paired data,
- Cross-modal reconstruction,
- Binary codes

FullText(HTML)

References(29)

References

[1]	GEETHA V and SUJATHA N. A survey on divergent classification of social media networking[C]. 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2022: 203–207. doi: 10.1109/ICCCIS56430.2022.10037606.
[2]	顾广华, 霍文华, 苏明月, 等. 基于非对称监督深度离散哈希的图像检索[J]. 电子与信息学报, 2021, 43(12): 3530–3537. doi: 10.11999/JEIT200988. GU Guanghua, HUO Wenhua, SU Mingyue, et al. Asymmetric supervised deep discrete hashing based image retrieval[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3530–3537. doi: 10.11999/JEIT200988.
[3]	GONG Yunchao, LAZEBNIK S, GORDO A, et al. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(12): 2916–2929. doi: 10.1109/TPAMI.2012.193.
[4]	DATAR M, IMMORLICA N, INDYK P, et al. Locality-sensitive hashing scheme based on p-stable distributions[C]. The 20th Annual Symposium on Computational Geometry, Brooklyn, USA, 2004: 253–262. doi: 10.1145/997817.997857.
[5]	SHEN Fumin, SHEN Chunhua, LIU Wei, et al. Supervised discrete hashing[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, 2015: 37–45. doi: 10.1109/CVPR.2015.7298598.
[6]	JI Rongrong, LIU Hong, CAO Liujuan, et al. Toward optimal manifold hashing via discrete locally linear embedding[J]. IEEE Transactions on Image Processing, 2017, 26(11): 5411–5420. doi: 10.1109/TIP.2017.2735184.
[7]	KOUTAKI G, SHIRAI K, and AMBAI M. Hadamard coding for supervised discrete hashing[J]. IEEE Transactions on Image Processing, 2018, 27(11): 5378–5392. doi: 10.1109/TIP.2018.2855427.
[8]	LIN Mingbao, JI Rongrong, LIU Hong, et al. Supervised online hashing via hadamard codebook learning[C]. The 26th ACM International Conference on Multimedia, Seoul, Republic of Korea, 2018: 1635–1643. doi: 10.1145/3240508.3240519.
[9]	LIN Mingbao, JI Rongrong, CHEN Shen, et al. Similarity-preserving linkage hashing for online image retrieval[J]. IEEE Transactions on Image Processing, 2020, 29: 5289–5300. doi: 10.1109/TIP.2020.2981879.
[10]	JIN Lu, LI Zechao, PAN Yonghua, et al. Weakly-supervised image hashing through masked visual-semantic graph-based reasoning[C]. The 28th ACM International Conference on Multimedia, Seattle, USA, 2020: 916–924. doi: 10.1145/3394171.3414022.
[11]	LI Zechao, TANG Jinhui, ZHANG Liyan, et al. Weakly-supervised semantic guided hashing for social image retrieval[J]. International Journal of Computer Vision, 2020, 128(8/9): 2265–2278. doi: 10.1007/s11263-020-01331-0.
[12]	SONG Jingkuan, YANG Yi, HUANG Zi, et al. Effective multiple feature hashing for large-scale near-duplicate video retrieval[J]. IEEE Transactions on Multimedia, 2013, 15(8): 1997–2008. doi: 10.1109/TMM.2013.2271746.
[13]	SHEN Xiaobo, SHEN Fumin, SUN Quansen, et al. Multi-view latent hashing for efficient multimedia search[C]. The 23rd ACM International Conference on Multimedia, Brisbane, Australia, 2015: 831–834. doi: 10.1145/2733373.2806342.
[14]	LIU Li, YU Mengyang, and SHAO Ling. Multiview alignment hashing for efficient image search[J]. IEEE Transactions on Image Processing, 2015, 24(3): 956–966. doi: 10.1109/TIP.2015.2390975.
[15]	LU Xu, LIU Li, NIE Liqiang, et al. Semantic-driven interpretable deep multi-modal hashing for large-scale multimedia retrieval[J]. IEEE Transactions on Multimedia, 2021, 23: 4541–4554. doi: 10.1109/TMM.2020.3044473.
[16]	YU Jun, HUANG Wei, LI Zuhe, et al. Hadamard matrix-guided multi-modal hashing for multi-modal retrieval[J]. Digital Signal Processing, 2022, 130: 103743. doi: 10.1016/j.dsp.2022.103743.
[17]	庾骏, 黄伟, 张晓波, 等. 基于松弛Hadamard矩阵的多模态融合哈希方法[J]. 电子学报, 2022, 50(4): 909–920. doi: 10.12263/DZXB.20210760. YU Jun, HUANG Wei, ZHANG Xiaobo, et al. Multimodal fusion hash learning method based on relaxed Hadamard matrix[J]. Acta Electronica Sinica, 2022, 50(4): 909–920. doi: 10.12263/DZXB.20210760.
[18]	LU Xu, ZHU Lei, CHENG Zhiyong, et al. Online multi-modal hashing with dynamic query-adaption[C]. The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 2019: 715–724. doi: 10.1145/3331184.3331217.
[19]	YU Jun, ZHANG Donglin, SHU Zhenqiu, et al. Adaptive multi-modal fusion hashing via hadamard matrix[J]. Applied Intelligence, 2022, 52(15): 17170–17184. doi: 10.1007/s10489-022-03367-w.
[20]	SHEN Xiaobo, SUN Quansen, and YUAN Yunhao. Semi-paired hashing for cross-view retrieval[J]. Neurocomputing, 2016, 213: 14–23. doi: 10.1016/j.neucom.2016.01.121.
[21]	WANG Di, SHANG Bin, WANG Quan, et al. Semi-paired and semi-supervised multimodal hashing via cross-modality label propagation[J]. Multimedia Tools and Applications, 2019, 78(17): 24167–24185. doi: 10.1007/s11042-018-6858-8.
[22]	GAO Jing, ZHANG Wenjun, ZHONG Fangming, et al. UCMH: Unpaired cross-modal hashing with matrix factorization[J]. Neurocomputing, 2020, 418: 178–190. doi: 10.1016/j.neucom.2020.08.029.
[23]	JING Rongrong, TIAN Hu, ZHANG Xingwei, et al. Self-Training based semi-Supervised and semi-Paired hashing cross-modal retrieval[C]. 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022: 1–8. doi: 10.1109/IJCNN55064.2022.9892301.
[24]	RASHTCHIAN C, YOUNG P, HODOSH M, et al. Collecting image annotations using amazon’s mechanical Turk[C]. The NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, America, 2010: 139–147.
[25]	CHUA T S, TANG Jinhui, HONG Richang, et al. NUS-WIDE: A real-world web image database from national university of Singapore[C]. The ACM International Conference on Image and Video Retrieval, Santorini, Greece, 2009: 48. doi: 10.1145/1646396.1646452.
[26]	ESCALANTE H J, HERNÁNDEZ C A, GONZALEZ J A, et al. The segmented and annotated IAPR TC-12 benchmark[J]. Computer Vision and Image Understanding, 2010, 114(4): 419–428. doi: 10.1016/j.cviu.2009.03.008.
[27]	WANG Daixin, CUI Peng, OU Mingdong, et al. Deep multimodal hashing with orthogonal regularization[C]. The 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015: 2291–2297.
[28]	YANG Rui, SHI Yuliang, and XU Xinshun. Discrete multi-view hashing for effective image retrieval[C]. 2017 ACM on International Conference on Multimedia Retrieval, Bucharest, Romania, 2017: 175–183. doi: 10.1145/3078971.3078981.
[29]	LU Xu, ZHU Lei, LIU Li, et al. Graph convolutional multi-modal hashing for flexible multimedia retrieval[C/OL]. The 29th ACM International Conference on Multimedia, Chengdu, China, 2021: 1414–1422. doi: 10.1145/3474085.3475598.