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RETRACTED ARTICLE: Simulation of cross-modal image-text retrieval algorithm under convolutional neural network structure and hash method

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This article was retracted on 14 February 2024

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Abstract

The purpose of this work is to quickly find useful information from the massive image database in view of the images, videos, and other multimedia data generated on Internet platforms, such as Wechat, Sina Weibo and Twitter. The hash algorithm is utilized to map image high-dimensional features into binary code strings to deal with problems of traditional image retrieval algorithms, such as high feature dimension, large storage space, and low retrieval efficiency. Besides, an end-to-end deep hash learning network is designed based on semantic preservation, and the deep Convolutional Neural Network (CNN) is adopted for image feature extraction and hash function learning. Moreover, a binary-constrained regularization term is added to the loss function, and a semantic reservation layer is supplemented to optimize the generation of hash code. Furthermore, a semi-supervised hash learning network is proposed based on Generative Adversarial Network (GAN), which takes the hash network as a discriminator, and a discriminating node is introduced into the output layer to discriminate the true and false samples. For each innovation of the algorithm, retrieval experiments are carried out on several public datasets to further verify the performance of the proposed algorithm to prove the effectiveness. The results reveal that: the loss function of adaptive weight based on hash network reduces the influence of imbalances positive and negative sample on retrieval performance, the constrained regular terms reduce the error caused by quantization, avoids information loss caused by "Relaxation" strategy in traditional methods, and optimizes hash structure. In this way, the obtained hash code can retain semantic similarity and improve the retrieval accuracy. Hash network based on GAN can improve the overall performance of the model by about 3%. Compared with the existing image retrieval algorithm, the deep hash learning algorithm has better effect in image retrieval than the current hash method. The study effectively solves the problems in the image and text information retrieval, and can provide some ideas and methods for the research of the text retrieval.

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References

  1. O’Connell GC, Alder ML, Smothers CG, Still CH, Webel AR, Moore SM (2020) Use of high-sensitivity digital ELISA improves the diagnostic performance of circulating brain-specific proteins for detection of traumatic brain injury during triage. Neurol Res 42(4):346–353

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tiggemann M, Anderberg I (2020) Social media is not real: the effect of ‘Instagram vs reality’images on women’s social comparison and body image. New Media Soc 22(12):2183–2199

    Article  Google Scholar 

  3. Kan S-C, Cen Y-G, Cen Y, Wang Y-H, Voronin V, Mladenovic V, Zeng M (2017) SURF binarization and fast codebook construction for image retrieval. J Vis Commun Image Represent 49:104–114

    Article  Google Scholar 

  4. Van Opijnen M, Santos C (2017) On the concept of relevance in legal information retrieval. Artif Intell Law 25(1):65–87

    Article  Google Scholar 

  5. Sun W, Cai Z, Li Y, Liu F, Fang S, Wang G (2018) Data processing and text mining technologies on electronic medical records: a review. J Healthc Eng 2018:256–273

    Article  Google Scholar 

  6. Piras L, Giacinto G (2017) Information fusion in content based image retrieval: a comprehensive overview. Inf Fusion 37:50–60

    Article  Google Scholar 

  7. Yu Z, Han H, Feng P, Zhao S, Zhou T, Kakade A, Kulshrestha S, Majeed S, Li X (2020) Recent advances in the recovery of metals from waste through biological processes. Bioresour Technol 297:122416–122429

    Article  CAS  PubMed  Google Scholar 

  8. Bellogín A, Castells P, Cantador I (2017) Statistical biases in Information Retrieval metrics for recommender systems. Inf Retr J 20(6):606–634

    Article  Google Scholar 

  9. Xu Y, Gong J, Xiong L, Xu Z, Wang J, Shi Y-Q (2017) A privacy-preserving content-based image retrieval method in cloud environment. J Vis Commun Image Represent 43:164–172

    Article  CAS  Google Scholar 

  10. Jonker D, Langevin S (2017) System and method for large scale information processing using data visualization for multi-scale communities. Google Patents 2017:2859–2864

    Google Scholar 

  11. Nguyen Mau T, Inoguchi Y (2020) Locality-sensitive hashing for information retrieval system on multiple GPGPU devices. Appl Sci 10(7):2539–2546

    Article  Google Scholar 

  12. Cheng S, Wang L, Du A (2019) An adaptive and asymmetric residual hash for fast image retrieval. IEEE Access 7:78942–78953

    Article  Google Scholar 

  13. Saritha RR, Paul V, Kumar PG (2019) Content based image retrieval using deep learning process. Clust Comput 22(2):4187–4200

    Article  Google Scholar 

  14. Al-Janabi S, Alkaim A, Al-Janabi E et al (2021) Intelligent forecaster of concentrations (PM2. 5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl 33:1–31

    Article  Google Scholar 

  15. Al-Janabi S, Salman AH (2021) Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications. Big Data Min Anal 4(2):124–138

    Article  Google Scholar 

  16. Al-Janabi S, Al-Shourbaji I (2016) A hybrid image steganography method based on genetic algorithm. In: 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). IEEE, pp 398–404

  17. Cheng Q, Gu X (2021) Bridging multimedia heterogeneity gap via Graph Representation Learning for cross-modal retrieval. Neural Netw 134:143–162

    Article  PubMed  Google Scholar 

  18. Xu G, Li X, Zhang Z (2020) Semantic consistency cross-modal retrieval with semi-supervised graph regularization. IEEE Access 8:14278–14288

    Article  Google Scholar 

  19. Lu X, Chen Y, Li X (2017) Hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features. IEEE Trans Image Process 27(1):106–120

    Article  MathSciNet  ADS  Google Scholar 

  20. Zhu L, Huang Z, Li Z, Xie L, Shen HT (2018) Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval. IEEE Trans Neural Netw Learn Syst 29(11):5264–5276

    Article  MathSciNet  PubMed  Google Scholar 

  21. Yan C, Gong B, Wei Y, Gao Y (2020) Deep multi-view enhancement hashing for image retrieval. IEEE Trans Pattern Anal Mach Intell 2020:2589–2603

    Google Scholar 

  22. Zheng X, Qiao X, Cao Y et al (2019) Content-aware generative modeling of graphic design layouts. ACM Trans Graph 38(4):1–15

    Article  Google Scholar 

  23. Mahdi MA, Al_Janabi S (2019) A novel software to improve healthcare base on predictive analytics and mobile services for cloud data centers. In: International Conference on Big Data and Networks Technologies. Springer, Cham, pp 320–339

  24. Al-Janabi S, Alkaim AF (2020) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput 24(1):555–569

    Article  Google Scholar 

  25. Al-Janabi S, Mohammad M, Al-Sultan A (2020) A new method for prediction of air pollution based on intelligent computation. Soft Comput 24(1):661–680

    Article  Google Scholar 

  26. Al-Janabi S, Alkaim AF, Adel Z (2020) An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24(14):10943–10962

    Article  Google Scholar 

  27. Tripathi A, Mishra KK, Tiwari S et al (2019) Nature inspired optimization algorithm for prediction of “minimum free energy” “RNA secondary structure.” J Reliab Intell Environ 5(4):241–257

    Article  Google Scholar 

  28. Ding K, Yang Z, Wang Y et al (2019) An improved perceptual hash algorithm based on u-net for the authentication of high-resolution remote sensing image. Appl Sci 9(15):2972

    Article  Google Scholar 

  29. Cheng SL, Wang LJ, Huang G et al (2019) A privacy-preserving image retrieval scheme based secure kNN, DNA coding and deep hashing. Multimed Tools Appl 80:1–23

    Google Scholar 

  30. Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep 10(1):1–12

    CAS  Google Scholar 

  31. Guo Y, Ding G, Liu L, Han J, Shao L (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process 26(3):1344–1354

    Article  MathSciNet  PubMed  ADS  Google Scholar 

  32. Han L, Li P, Bai X, Grecos C, Zhang X, Ren P (2020) Cohesion intensive deep hashing for remote sensing image retrieval. Remote Sens 12(1):101–113

    Article  ADS  Google Scholar 

  33. Jin KH, McCann MT, Froustey E, Unser M (2017) Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process 26(9):4509–4522

    Article  MathSciNet  ADS  Google Scholar 

  34. Gros D, Habermann T, Kirstein G, Meschede C, Ruhrberg SD, Schmidt A, Siebenlist T (2018) Anaphora resolution: analysing the impact on mean average precision and detecting limitations of automated approaches. Int J Inf Retr Res 8(3):33–45

    Google Scholar 

  35. Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295

    Article  Google Scholar 

  36. Ding Y, Wong WK, Lai Z, Zhang Z (2020) Discriminative dual-stream deep hashing for large-scale image retrieval. Inf Process Manag 57(6):102288–102296

    Article  Google Scholar 

  37. Cai Y, Li Y, Qiu C et al (2019) Medical image retrieval based on convolutional neural network and supervised hashing. IEEE Access 7:51877–51885

    Article  Google Scholar 

  38. Tang J, Li Z, Zhu X (2018) Supervised deep hashing for scalable face image retrieval. Pattern Recogn 75:25–32

    Article  ADS  Google Scholar 

  39. Xu R, Li C, Yan J et al (2019) Graph convolutional network hashing for cross-modal retrieval. IJCAI 2019:982–988

    Google Scholar 

  40. Wang S, Wang R, Yao Z et al (2020) Cross-modal scene graph matching for relationship-aware image-text retrieval. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 1508–1517

  41. Dong X, Zhang H, Dong X et al (2021) Iterative graph attention memory network for cross-modal retrieval. Knowl-Based Syst 226:107138–107143

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Technology, Beihua University, Jilin City, 132000, China

    XianBen Yang & Wei Zhang

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  1. XianBen Yang
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  2. Wei Zhang
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Correspondence to Wei Zhang.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11227-024-05965-6"

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Yang, X., Zhang, W. RETRACTED ARTICLE: Simulation of cross-modal image-text retrieval algorithm under convolutional neural network structure and hash method. J Supercomput 78, 7106–7132 (2022). https://doi.org/10.1007/s11227-021-04157-w

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  • DOI: https://doi.org/10.1007/s11227-021-04157-w

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