Skip to main content
SpringerLink
Log in

Multimedia image and video retrieval based on an improved HMM

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

In today's information age, information is gathered from text and more complex media, such as images, audio, and video. Among these data sources, the rapid growth of video information has led to it to gradually become the main source of information in people's lives. Video information is characterized by many kinds of information, complex forms, and a low degree of structure. Therefore, effectively classifying, managing and retrieving video information has become a difficult problem to solve. In this paper, an improved crow search algorithm is used to process video images, and the information entropy is used to extract the key frames, which reduces the computation burden of each frame feature calculation and feature contrast process, thus shortening the key frame detection time. Then, all the feature sets are extracted and used as input for an HMM according to the observed sequence \(O = O_{1} ,O_{2} ,O_{3} , \cdot \cdot \cdot ,O_{T}\) of the input image or video data and the initial model parameters \(\lambda = (\pi ,A,B)\). According to the training rules, the model parameters are repeatedly adjusted and modified, and the new model \(\overline{\lambda }\) is constructed step by step to realize the retrieval of multimedia images and videos. The experimental results show that the method has obvious advantages in terms of the retrieval time and retrieval effect and provides new ideas for multimedia image and video retrieval.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Kumar, M., Mao, Y.H., Wang, Y.H., Qiu, T.R., Yang, C., Zhang, W.P.: Fuzzy theoretic approach to signals and systems: static systems. Inform Sci 418, 668–702 (2017)

    Article  MATH  Google Scholar 

  2. Zhang, W.P., Yang, J.Z., Fang, Y.L., Chen, H.Y., Mao, Y.H., Kumar, M.: Analytical fuzzy approach to biological data analysis. Saudi J Biol Sci 24(3), 563–573 (2017)

    Article  Google Scholar 

  3. Zhang, C., Jingbing, L.I., Wang, S., et al.: Encrypted image retrieval algorithm based on discrete wavelet transform and perceptual hash. J Comput Appl 38(2), 539–544 (2018)

    Google Scholar 

  4. Ashraf, R., Ahmed, M., Jabbar, S., et al.: Content based image retrieval by using color descriptor and discrete wavelet transform. J Med Syst 42(3), 44 (2018)

    Article  Google Scholar 

  5. Khatami, A., Babaie, M., Khosravi, A., et al.: Parallel deep solutions for image retrieval from imbalanced medical imaging archives[J]. Appl Soft Comput 63, 197–205 (2018)

    Article  Google Scholar 

  6. Mehmood, Z., Abbas, F., Mahmood, T., et al.: Content-based image retrieval based on visual words fusion versus features fusion of local and global features[J]. Arab J Sci Eng 9, 1–20 (2018)

    Google Scholar 

  7. Nath, V.K., Hatibaruah, R., Hazarika, D.: An efficient multiscale wavelet local binary pattern for biomedical image retrieval. In: Proceedings of the international conference on computing and communication systems, vol. 24, p. 489. Springer, Singapore (2018)

    Chapter  Google Scholar 

  8. Banerjee, P., Bhunia, A.K., Bhattacharyya, A., et al.: Local neighborhood intensity pattern—a new texture feature descriptor for image retrieval. Expert Syst Appl 113, 100–115 (2018)

    Article  Google Scholar 

  9. Vassou, S.A., Anagnostopoulos, N., Christodoulou, K., et al.: CoMo: a scale and rotation invariant compact composite moment-based descriptor for image retrieval[J]. Multim Tools Appl 1, 1–24 (2018)

    Google Scholar 

  10. Lokoc, J., Bailer, W., Schoeffmann, K., et al.: On influential trends in interactive video retrieval: video browser showdown 2015–2017[J]. IEEE Trans Multim 99, 1–1 (2018)

    Google Scholar 

  11. Joolee J B, Lee YK. Video retrieval based on image queries using THOG for augmented reality environments. In: IEEE international conference on big data and smart computing. IEEE Computer Society, pp. 557–560 (2018)

  12. Kumar, G.S.N., Reddy, V.S.K., Kumar, S.S.: High-performance video retrieval based on spatio-temporal features. In: Microelectronics, electromagnetics and telecommunications, pp. 433–441. Springer, Singapore (2018)

    Chapter  Google Scholar 

  13. Dong, J., Li, X., Snoek, C.G.M.: Predicting visual features from text for image and video caption retrieval. IEEE Trans Multim 99, 1–1 (2018)

    Article  Google Scholar 

  14. Kogami, J., Tomiyama, K., Miyaji, Y.: Kansei generator using hmm for virtual kansei in caretaker support robot. Kansei Eng Int 8(1), 83–90 (2009)

    Article  Google Scholar 

  15. Chenglang, L.U., Zongda, W.U., Guiling, L.I.: Design and implementation of MPEG-7-based video content retrieval system. J Northw Univ 48(3), 369–375 (2018)

    Google Scholar 

  16. Hao, L., Nandiganahalli, J.S., Hwang, I.: Automation intent inference using the GFHMM for flight deck mode confusion detection. J Aerosp Informat Syst 15(6), 1–6 (2018)

    Google Scholar 

  17. Han, M., Li, X., Zhang, S., et al.: Generation of soliton bursts with flexibly controlled pulse intervals based on the dispersive Fourier-transform technique. IEEE J Sel Top Quant Elect. 99, 1–1 (2018)

    Google Scholar 

  18. Jian-Tai, W.U., Liu, G.J., Liu, W.W., et al.: Cyber security situation evaluation method based on association analysis and hidden Markov model. Comput Modernizat 6, 9 (2018)

    Google Scholar 

  19. Kang, H.E., Zhao, Z.Z., Xiao-Biao, L.I.: Hidden Markov model-based workpiece surface quality monitoring research. Yinshan Acad J 32(3), 8–12 (2018)

    Google Scholar 

  20. Wang, G., Chen, J., Hong, R., et al.: Model for the positional accuracy degradation of NC rotary tables based on the hidden Markov model and optimized particle filtering. J Vibrat Shock 37(6), 7–13 (2018)

    Google Scholar 

  21. Sitnikova, T.A., Hughes, J.W., Ahlfors, S.P., et al.: Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease. Neuroimage Clin 20, 128–152 (2018)

    Article  Google Scholar 

  22. Zhang, H., Ji, Y., Huang, W., et al.: Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl 31, 7361–7380 (2019)

    Article  Google Scholar 

  23. Rahmani, F., Zargari, F.: Temporal feature vector for video analysis and retrieval in high efficiency video coding compressed domain. Electron Lett 54(5), 294–295 (2018)

    Article  Google Scholar 

  24. Zhou-Miao, L.U.: Discussion on the technology of massive continuous video data retrieval. Digital Technol Appl 36(1), 220–222 (2018)

    Google Scholar 

  25. Poornima, N., Saleena, B.: Multi-modal features and correlation incorporated Naive Bayes classifier for a semantic-enriched lecture video retrieval system. Imaging Sci J 66(9), 1–15 (2018)

    Google Scholar 

  26. Xie, L., Zhang, L., Jian, L.I.: Rapid analysis and retrieval of massive video data in nature reserves. Comput Syst Appl 27(4), 63–69 (2018)

    Google Scholar 

  27. Jing, C., Dong, Z., Pei, M., et al.: Heterogeneous hashing network for face retrieval across image and video domains. IEEE Trans Multim. 99, 1–1 (2018)

    Google Scholar 

  28. Cheong, C.W., Lim, W.S., See, J.: Vehicle Semantics Extraction and Retrieval for Long-Term Carpark Video Surveillance. In: International Conference on Multimedia Modeling, pp. 315–326. Springer, Cham (2018)

    Chapter  Google Scholar 

  29. Fa-Ping, L.I.: Design of intelligent retrieval system for ship cabin monitoring video. Ship Sci Technol 40(6), 199–201 (2018)

    Google Scholar 

  30. Nasreen, A., Vinutha, H., Shobha, G.: Analysis of video content through object search using SVM classifier. Innovations in electronics and communication engineering, pp. 325–333. Springer, Singapore (2018)

    Chapter  Google Scholar 

Download references

Acknowledgements

This study was partially funded by The Institute of Tibetan Plateau Research, Chinese Academy of Sciences.

Author information

Authors and Affiliations

  1. Key Laboratory of Continental Collision and Plateau Uplift, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100085, China

    Yanbing Liu & Sanjev Dhakal

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yanbing Liu & Sanjev Dhakal

  3. Chengdu University of Technology, Chengdu, 610059, China

    Binyao Hao

Authors
  1. Yanbing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjev Dhakal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binyao Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanbing Liu.

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

Liu, Y., Dhakal, S. & Hao, B. Multimedia image and video retrieval based on an improved HMM. Multimedia Systems 28, 2093–2103 (2022). https://doi.org/10.1007/s00530-020-00686-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-020-00686-1

Keywords

Search

Navigation