Skip to main content
Log in

Movie trailer classification using deer hunting optimization based deep convolutional neural network in video sequences

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In current situation, video classification is one of the important research domains. Since video is a complex media with various components, classification of video is normally a complex process. This paper presented a human action based movie trailer classification using optimized deep convolutional neural network in video sequences. Initially, images are converted into the grayscale conversion. Using the adaptive median filtering process, the pre-processing stage is accomplished. Threshold based segmentation approach is utilized for subtracting the background from the video frames and to extract the foreground portion. In the feature extraction stage, the visual features (color and texture features) and motion features are extracted from the segmented portion. Finally, the mined features are trained and classified with the help of optimized deep convolutional neural network (DCNN) for the movie trailer classifications. Here, the deer hunting optimization (DHO) is introduced to optimize the weight values of DCNN. The proposed (DCNN-DHO) human action based movie trailer classification is executed in the MATLAB environment. The experimental results are evaluated and compared with the existing methods in terms of accuracy, false alarm rate, sensitivity, specificity, precision, F-measure and false discovery rate. The results of the proposed method are compared with filtering process and without filtering process in which 95.23% of accuracy is achieved for the suggested approach with filtering and 90.91% of accuracy is achieved for the suggested approach without filtering process.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Liu A-A, Su Y-T, Nie W-Z, Kankanhalli M (2016) Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 39(1):102–114

    Google Scholar 

  2. Kumar V, Namboodiri A, Jawahar CV (2018) Semi-supervised annotation of faces in image collection, signal. Image and Video Processing 12(1):141–149

    Google Scholar 

  3. Ou W, Luan X, Gou J, Zhou Q, Xiao W, Xiong X, Zeng W (2018) Robust discriminative nonnegative dictionary learning for occluded face recognition. Pattern Recogn Lett 107:41.49

    Google Scholar 

  4. Qin Z, Shelton CR (2017) Event detection in continuous video: An inference in point process approach. IEEE Trans Image Process 26(12):5680–5691

    MathSciNet  MATH  Google Scholar 

  5. Choros K (2018) Video genre classification based on length analysis of temporally aggregated video shots, computational collective intelligence, 509-518

  6. Chu W-T and Guo H-J (2017) Movie genre classification based on poster images with deep neural networks. In Proceedings of the Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes, 39–45

  7. Singhal A, Kumar P, Saini R, Roy PP, Dogra DP, Kim BG (2018) Summarization of videos by analysing affective state of the user through crowdsource. Cogn Syst Res 52:917–930

    Google Scholar 

  8. Singh J, Goyal G, Gupta S (2018) FADU-EV an automated framework for pre-release emotive analysis of theatrical trailers, multimedia tools and applications, 1-8

  9. Kar A, Rai N, Sikka K and Sharma G (2017) Adascan: adaptive scan pooling in deep convolutional neural networks for human action recognition in videos." in proceedings of the IEEE conference on computer vision and pattern recognition, 3376-3385.

  10. Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2017) Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access 6:1155–1166

    Google Scholar 

  11. Wei P, Sun H, Zheng N (2019) Learning composite latent structures for 3D human action representation and recognition. IEEE Transactions on Multimedia 21:2195–2208

    Google Scholar 

  12. Zhang P, Lan C, Xing J, Zeng W, Xue J and Zheng N (2019) View adaptive neural networks for high performance skeleton-based human action recognition." IEEE transactions on pattern analysis and machine intelligence

  13. Li F, Shuang F, Liu Z, Qian X (2018) A cost-constrained video quality satisfaction study on mobile devices. IEEE Transactions on Multimedia 20(5):1154–1168

    Google Scholar 

  14. Tian Y, Kong Y, Ruan Q, An G, Fu Y (2017) Hierarchical and spatio-temporal sparse representation for human action recognition. IEEE Trans Image Process 27(4):1748–1762

    MathSciNet  Google Scholar 

  15. De Amorim MN, Saleme EB, de Assis Neto FR, Santos CA, Ghinea G (2019) Crowdsourcing authoring of sensory effects on videos. Multimed Tools Appl 78(14):19201–19227

    Google Scholar 

  16. Acar E, Hopfgartner F, Albayrak S (2016) A comprehensive study on mid-level representation and ensemble learning for emotional analysis of video material. Multimed Tools Appl 76(9):11809–11837

    Google Scholar 

  17. Ben-Ahmed O and Huet B (2018) Deep multimodal features for movie genre and interestingness prediction, 2018 international conference on content-based multimedia indexing (CBMI)

  18. Ogawa T, Sasaka Y, Maeda K, Haseyama M (2018) Favourite video classification based on multimodal bidirectional LSTM. IEEE Access 6:61401–61409

    Google Scholar 

  19. Simoes GS, Wehrmann J, Barros RC, Ruiz DD (2016) Movie genre classification with convolutional neural networks. InNeural networks (IJCNN), 2016 international joint conference on IEEE 259-266

  20. Wehrmann J, Barros R (2017) Movie genre classification: a multi-label approach based on convolutions through time. Appl Soft Comput 61:973–982

    Google Scholar 

  21. Shrestha S (2014) Image denoising using new adaptive based median filters. arXiv preprint arXiv:1410. 2175

  22. Al-Amri SS and Kalyankar NV (2010) Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020

  23. Malakar A and Mukherjee J (2013) Image clustering using color moments, histogram, edge and K-means clustering. International journal of science and research (IJSR), India online ISSN 2319-7064

  24. Dandotiya Y, Atre A (2017) Image retrieval using edge detection, RLBP, color moment method for YCbCr and HSV color space. Communication and Aerospace Technology, IEEE 2:662–668

    Google Scholar 

  25. Yoo J, Lee G-c (2019) Moving object detection using an object motion reflection model of motion vectors. Symmetry 11(1):34

    Google Scholar 

  26. Zhang W, Xu L, Li Z, Lu Q, Liu Y (2016) A deep-intelligence framework for online video processing. IEEE Softw 33(2):44–51

    Google Scholar 

  27. Ye Z, Hu Z, Wang H, Chen H (2011 May 28) Automatic threshold selection based on artificial bee colony algorithm. In 2011 3rd international workshop on intelligent systems and applications, IEEE 1-4

  28. Brammya G, Praveena S, Ninu Preetha NS, Ramya R, Rajakumar BR, Binu D (2019 May 24) Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput J

  29. Yue-Hei NJ, Hausknecht M, Vijayanarasimhan S, Vinyals O, Monga R and Toderici G (2015) Beyond short snippets: deep networks for video classification. In proceedings of the IEEE conference on computer vision and pattern recognition, 4694-4702

  30. Babaee M, Dinh DT, Rigoll G (2018) A deep convolutional neural network for video sequence background subtraction. Pattern Recogn 76:635–649

    Google Scholar 

  31. McLaughlin N, del Rincon JM and Miller P (2016) Recurrent convolutional network for video-based person re-identification. In proceedings of the IEEE conference on computer vision and pattern recognition, 1325-1334

  32. Seo Y-S, Huh J-H (2019) Automatic emotion-based music classification for supporting intelligent IoT applications. Electronics 8(2):164

    Google Scholar 

  33. Liu Z, Zhang C, Tian Y (2016) 3D-based deep convolutional neural network for action recognition with depth sequences. Image Vis Comput 55:93–100

    Google Scholar 

  34. Wang N, Yeung D-Y (2013) Learning a deep compact image representation for visual tracking. In Advances in neural information processing systems:809–817

  35. Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona PO (2015) Action recognition from depth maps using deep convolutional neural networks. IEEE Transactions on Human-Machine Systems 46(4):498–509

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prashant Giridhar Shambharkar.

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

Shambharkar, P.G., Doja, M.N. Movie trailer classification using deer hunting optimization based deep convolutional neural network in video sequences. Multimed Tools Appl 79, 21197–21222 (2020). https://doi.org/10.1007/s11042-020-08922-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08922-6

Keywords

Navigation