Skip to main content

Advertisement

Springer Nature Link
Log in

Deep neural combinational model (DNCM): digital image descriptor for child’s independent learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This project is an endeavor to address preschool children’s independent learning. Currently, technology is invading our lives, and working parents are busily overpowering their social setup. As a result, the art of preschool education to young children has become rare or vanished. Automatic image descriptors (captioning models) have recently shown their effectiveness, motivating us to utilize such models for address purposes. Unfortunately, developed image descriptors produce only complex and generic visual descriptions irrelevant to children’s understanding. Therefore, it is important to have a suitable image descriptor as teaching material for young children at the initial educational stage. To fill this gap, we introduced a novel digital image descriptor and 3k-Flickr-SDD dataset using smart augmentation that originally extracted and labeled solitary dogs’ images from Flickr8k and Stanford Dogs Dataset (SDD) datasets. The newly developed 3k-Flickr-SDD dataset split further into two versions, making it meet the standard experimental requirements. The proposed method accumulates Convolutional Neural Networks (CNNs) for image contents extraction, whereas; Long Short-term Memory (LSTM) language model customizes to generate understandable and attractive text from Dogs images. We performed the quantitative and qualitative analysis; the finding reveals that the proposed model outperforms in contrast to existing models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Barnett WS (1992) Benefits of compensatory preschool education. J Hum Resour 27:279–312

    Article  Google Scholar 

  2. Callison-Burch C, Osborne M, Koehn P (2006) Re-evaluation of the role of bleu in machine translation research. In: 11th Conference of the European Chapter of the Association for Computational Linguistics

  3. Chang YS (2018) Fine-grained attention for image caption generation. Multimed Tools Appl 77:2959–2971

    Article  Google Scholar 

  4. Chen L, Zhang H, Xiao J, Nie L, Shao J, Liu W, Chua TS (2017) Sca-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 5659–5667

  5. Chen J, Dong W, Li M Image caption generator based on deep neural networks

  6. Cheng Q, Zhang Q, Fu P, Tu C, Li S (2018) A survey and analysis on automatic image annotation. Pattern Recogn 79:242–259

    Article  Google Scholar 

  7. Cui Y, Yang G, Veit A, Huang X, Belongie S (2018) Learning to evaluate image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 5804–5812

  8. Degadwala S, Vyas D, Biswas H, Chakraborty U, Saha S (2021) Image captioning using inception V3 transfer learning model. In: 2021 6th International Conference on Communication and Electronics Systems (ICCES). IEEE, pp 1103–1108

  9. Denoual E, Lepage Y (2005) BLEU in characters: towards automatic MT evaluation in languages without word delimiters. In: Companion Volume to the Proceedings of Conference including Posters/Demos and Tutorial Abstracts

  10. Donahue J, Hendricks LA, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2625–2634

  11. Farhadi A, Hejrati M, Sadeghi MA, Young P, Rashtchian C, Hockenmaier J, Forsyth D (2010) Every picture tells a story: generating sentences from images. In: European conference on computer vision. Springer, Berlin, pp 15–29

    Google Scholar 

  12. Fu K, Jin J, Cui R, Sha F, Zhang C (2017) Aligning where to see and what to tell: image captioning with region-based attention and scene-specific contexts. IEEE Trans Pattern Anal Mach Intell 39(12):2321–2334

    Article  Google Scholar 

  13. Gong Y, Wang L, Hodosh M, Hockenmaier J, Lazebnik S (2014) Improving image-sentence embeddings using large, weakly annotated photo collections. In: European conference on computer vision. Springer, Cham, pp 529–545

    Google Scholar 

  14. Gupta N, Jalal AS (2020) Integration of textual cues for fine-grained image captioning using deep CNN and LSTM. Neural Comput & Applic 32(24):17899–17908

    Article  Google Scholar 

  15. Hibbin R (2016) The psychosocial benefits of oral storytelling in school: developing identity and empathy through narrative. Pastor Care Educ 34(4):218–231

    Article  Google Scholar 

  16. Hodosh M, Young P, Hockenmaier J (2013) Framing image description as a ranking task: data, models, and evaluation metrics. J Artif Intell Res 47:853–899

    Article  MathSciNet  Google Scholar 

  17. Hossain M, Sohel F, Shiratuddin MF, Laga H (2018) A comprehensive study of deep learning for image captioning. arXiv preprint arXiv:1810.04020

  18. Jent JF, Niec LN, Baker SE (2011) Play and interpersonal processes, Play in clinical practice: evidence-based approaches. Guilford Press, New York

    Google Scholar 

  19. Karpathy A, Fei-Fei L (2015). Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3128–3137

  20. Khan MF, Sadiq-Ur-Rahman SM, Islam MS (2021) Improved Bengali image captioning via deep convolutional neural network based encoder-decoder model. In: Proceedings of International Joint Conference on Advances in Computational Intelligence. Springer, Singapore, pp 217–229

    Chapter  Google Scholar 

  21. Khosla A, Jayadevaprakash N, Yao B, Li FF (2011) Novel dataset for fine-grained image categorization: Stanford dogs. In: Proc. CVPR Workshop on Fine-Grained Visual Categorization (FGVC), vol. 2, no. 1

  22. Kinghorn P, Zhang L, Shao L (2018) A region-based image caption generator with refined descriptions. Neurocomputing 272:416–424

    Article  Google Scholar 

  23. Kiros R, Salakhutdinov R, Zemel RS (2014) Unifying visual-semantic embeddings with multimodal neural language models. arXiv preprint arXiv:1411.2539

  24. Kuznetsova P, Ordonez V, Berg TL, Choi Y (2014) Treetalk: composition and compression of trees for image descriptions. Trans Assoc Computat Linguist 2:351–362

    Article  Google Scholar 

  25. Lemley J, Bazrafkan S, Corcoran P (2017) Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5:5858–5869

    Article  Google Scholar 

  26. Li L, Tang S, Zhang Y, Deng L, Tian Q (2018) GLA: global-local attention for image description. IEEE Trans Multimed 20:726–737

    Article  Google Scholar 

  27. Lin CY (2004) Rouge: a package for automatic evaluation of summaries. Text Summarization Branches Out

  28. Mao J, Xu W, Yang Y, Wang J, Huang Z, Yuille A (2014) Deep captioning with multimodal recurrent neural networks (m-rnn). arXiv preprint arXiv:1412.6632

  29. Naqvi N, Ye Z (2020) Image captions: global-local and joint signals attention model (GL-JSAM). Multimed Tools Appl 79:24429–24448. https://doi.org/10.1007/s11042-020-09128-6

    Article  Google Scholar 

  30. Papineni K, Roukos S, Ward T, Zhu WJ (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 311–318

  31. Perry BD, Szalavitz M (2010) Born for love: why empathy is essential—and endangered. HarperCollins e-Books

  32. Minoofam SAH, Bastanfard A, Keyvanpour MR (2021) TRCLA: a transfer learning approach to reduce negative transfer for cellular learning automata. In: IEEE transactions on neural networks and learning systems. IEEE. https://doi.org/10.1109/TNNLS.2021.3106705

  33. Shah P, Bakrola V, Pati S (2017) Image captioning using deep neural architectures. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, pp 1–4

  34. Soh M (2016) Learning CNN-LSTM architectures for image caption generation. Dept. Comput. Sci., Stanford Univ., Stanford, CA, USA, Tech. Rep

  35. Sun C, Gan C, Nevatia R (2015) Automatic concept discovery from parallel text and visual corpora. In: Proceedings of the IEEE international conference on computer vision. pp 2596–2604

  36. Venter E (2017) Bridging the communication gap between Generation Y and the Baby Boomer generation. Int J Adolesc Youth 22(4):497–507. https://doi.org/10.1080/02673843.2016.1267022

    Article  MathSciNet  Google Scholar 

  37. Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3156–3164

  38. Wang C, Yang H, Meinel C (2018) Image captioning with deep bidirectional lstms and multi-task learning. ACM Trans Multimed Comput Commun Appl 14(2s):40

    Google Scholar 

  39. Warin J (2011) Stories of self: tracking children's identity and wellbeing through the years of school. Educ Health 29(1):19–20

    MathSciNet  Google Scholar 

  40. Wu Q, Shen C, Wang P, Dick A, van den Hengel A (2018) Image captioning and visual question answering based on attributes and external knowledge. IEEE Trans Pattern Anal Mach Intell 40(6):1367–1381

    Article  Google Scholar 

  41. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel RS, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning. pp 2048–2057

  42. Yao T, Pan Y, Li Y, Qiu Z, Mei T (2017) Boosting image captioning with attributes. In Proceedings of the IEEE International Conference on Computer Vision. pp 4894–4902

  43. Ye Z, Khan R, Naqvi N, Islam MS (2021) A novel automatic image caption generation using bidirectional long-short term memory framework. Multimed Tools Appl 80:25557–25582. https://doi.org/10.1007/s11042-021-10632-6

    Article  Google Scholar 

  44. Yu F, Ip HH (2006) Automatic semantic annotation of images using spatial hidden Markov model. In: 2006 IEEE International Conference on Multimedia and Expo. IEEE pp 305–308

  45. Zhao D, Chang Z, Guo S (2019) A multimodal fusion approach for image captioning. Neurocomputing 329:476–485

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by China’s National Natural Science Foundation (No. 61671418) and the Advanced Research Fund of the University of Science and Technology of China. Conflicts of Interest: The authors declare no conflict of interest.

Author information

Authors and Affiliations

  1. University of Science and Technology of China (USTC), Hefei, People’s Republic of China

    Nuzhat Naqvi, M. Shujah Islam, Mansoor Iqbal, Shamsa Kanwal & ZhongFu Ye

  2. School of Computer Science, South China Normal University, Guangzhou, People’s Republic of China

    Asad Khan

Authors
  1. Nuzhat Naqvi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. M. Shujah Islam
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mansoor Iqbal
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shamsa Kanwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Asad Khan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. ZhongFu Ye
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nuzhat Naqvi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naqvi, N., Islam, M.S., Iqbal, M. et al. Deep neural combinational model (DNCM): digital image descriptor for child’s independent learning. Multimed Tools Appl 81, 29955–29975 (2022). https://doi.org/10.1007/s11042-022-12291-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12291-7

Keywords