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An optimization framework of video advertising: using deep learning algorithm based on global image information

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Abstract

Object detection and recognition technology plays an important role in video advertising. Based on the deep learning algorithm, this paper first builds a real-time object detection model (Yes-Net) based on image global information which combines the advantages of CNN and RNN algorithm together and has achieved significant effects compared with other object detection algorithms. We used this model to build an accurate video advertising framework and applied the framework in an IPTV platform of a telecommunication operator to collect comparative data. We first performed image detection and recognition in video playing, and then associated the recognition results with the ads database to conduct real-time classification and accurate ads delivery. It proves that compared with traditional advertisement delivery method, this new framework dramatically improves the click-through rate, and achieves better results.

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References

  1. Affonso, C., Rossi, A., Vieira, F., de Carvalho, A.: Deep learning for biological image classification. Expert Syst. Appl 85, 114–122 (2017)

    Article  Google Scholar 

  2. Bao, L., Le, D.-N., Nguyen, G.N., Bhateja, V., Satapathy, S.C.: Optimizing feature selection in video-based recognition using Max-Min Ant System for the online video contextual advertisement user-oriented system. J. Comput. Sci. 21, 361–370 (2017)

    Article  Google Scholar 

  3. Bourbakis, N., Esposito, A., Kavraki, D.: Extracting and associating meta-features for understanding people’s emotional behaviour: face and speech. Cognit. Comput. 3(3), 436–448 (2011)

    Article  Google Scholar 

  4. Castaldo, F., Palmieri, F.A., Regazzoni, C.S.: Bayesian analysis of behaviors and interactions for situation awareness in transportation systems. IEEE Trans. Intell. Transp. Syst. 17(2), 313–322 (2016)

    Article  Google Scholar 

  5. Chan, T.-H., Jai, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: PCANet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)

    Article  MathSciNet  Google Scholar 

  6. Chen, X.Y., Xiang, S.M., Liu, C.L., Pan, C.H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11(10), 1797–1801 (2014)

    Article  Google Scholar 

  7. Dong, C., He, K.M., Tang, X.O.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

    Article  Google Scholar 

  8. Duffner, S., Garcia, C.: Visual focus of attention estimation with unsupervised incremental learning. IEEE Trans. Circuits Syst. Video Technol. 26(12), 2264–2272 (2016)

    Article  Google Scholar 

  9. Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9(12), 2170–2179 (2014)

    Article  Google Scholar 

  10. Farabet, C., Couprie, C., Najman, L., Lecun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915 (2013)

    Article  Google Scholar 

  11. Geurin-Eagleman, A.N., Burch, L.M.: Communicating via photographs: a gendered analysis of olympic athletes’ visual self-presentation on instagram. Sport Manag. Rev. 19(2), 133–145 (2016)

    Article  Google Scholar 

  12. Girshick, R., Donahue, J., Darrell, T.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

  13. Girshick, R., Iandola, F., Darrell, T., Malik, J.: Deformable part models are convolutional neural networks. In: 2015 IEEE Conference on CVPR, pp. 437–446 (2015)

  14. Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

  15. Greene, M.R., Baldassano, C., Esteva, A., Beck, D.M., Li, F.F., Gauthier, I.: Visual scenes are categorized by function. J. Exp. Psychol. 145(1), 82–94 (2016)

    Article  Google Scholar 

  16. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–80 (1997)

    Article  Google Scholar 

  17. Hou, S.J., Chen, L., Tao, D.C., Zhou, S.B., Liu, W.J., Zheng, Y.J.: Multi-layer multi-view topic model for classifying advertising video. Pattern Recognit. 68, 66–81 (2017)

    Article  Google Scholar 

  18. Hsieh, L.C., Wu, G.L., Hsu, Y.M., Hsu, W.: Online image search result grouping with MapReduce-based image clustering and graph construction for large-scale photos. J. Vis. Commun. Image R. 25(2), 384–395 (2014)

    Article  Google Scholar 

  19. Huang, F.L., Zhang, S.C., Zhang, J.L., Yu, G.: Multimodal learning for topic sentiment analysis in microblogging. Neurocomputing 253, 144–153 (2017)

    Article  Google Scholar 

  20. Ji, S.W., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Article  Google Scholar 

  21. Jiang, Y., Wang, Y.J.: Psychologically inspired visual information storage and retrieval modeling for multiclass image classification. Neurocomputing 259, 194–200 (2017)

    Article  Google Scholar 

  22. Jiji, G., Durai Raj, P.: Content-based image retrieval in dermatology using intelligent technique. IET Image Process. 9(4), 306–317 (2015)

    Article  Google Scholar 

  23. Kaneko, T., Yanai, K.: Event photo mining from Twitter using keyword bursts and image clustering. Neurocomputing 172, 143–158 (2016)

    Article  Google Scholar 

  24. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  25. Lee, H., Grosse, R., Ranganath, R., Ng, A.: Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun. ACM 54(10), 95–103 (2011)

    Article  Google Scholar 

  26. Li, K., Zou, C.Q., Bu, S.H., Liang, Y., Zhang, J., Gong, M.L.: Multi-modal feature fusion for geographic image annotation. Pattern Recognit. 73, 1–14 (2018)

    Article  Google Scholar 

  27. Li, R.F., Feng, F.X., Ahmad, I., Wang, X.J.: Retrieving real world clothing images via multi-weight deep convolutional neural networks. Cluster Comput. (2017). https://doi.org/10.1007/s10586-017-1052-8

  28. Muhammad, A., Tamleek, A.T., Shakirullah S., Muhammad, A., Muhammad, S.: DeepSIC: a deep model for satellite image classification. Cluster Comput. (2017). https://doi.org/10.1007/s10586-017-1010-5

  29. Murugappan, V., Sabeenian, R.S.: Texture based medical image classification by using multi-scale gabor rotation-invariant local binary pattern (MGRLBP). Clust. Comput. 1–14(2017)

  30. Panteras, G., Wise, S., Lu, X., Croitoru, A., Crooks, A., Stefanidis, A.: Triangulating social multimedia content for event localization using Flickr and Twitter. Trans. GIS 19(5), 694–715 (2015)

    Article  Google Scholar 

  31. Qawaqneh, Z., Mallouh, A.A., Barkana, B.D.: Age and gender classification from speech and face images by jointly fine-tuned deep neural networks. Expert Syst. Appl. 85, 76–86 (2017)

    Article  Google Scholar 

  32. Qayyum, A., Anwar, S., Awais, M., Majid, M.: Medical image retrieval using deep convolutional neural network. Neurocomputing 266, 8–20 (2017)

    Article  Google Scholar 

  33. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on CVPR, pp. 779–788 (2016)

  34. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  35. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Li, F.F.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  36. Shin, H.-C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imag. 35(5), 1285–1298 (2016)

  37. Shin, H.-C., Orton, M.R., Collins, D.J., Doran, S.J., Leach, M.O.: Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1930–1943 (2013)

  38. Tang, J.X., Deng, C.W., Huang, G.B., Zhao, B.J.: Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans. Geosci. Remote Sens. 53(3), 1174–1185 (2015)

    Article  Google Scholar 

  39. Troya-Galvis, A., Gançarski, P., Berti-Équille, L.: Remote sensing image analysis by aggregation of segmentation-classification collaborative agents. Pattern Recognit. 73, 259–274 (2018)

    Article  Google Scholar 

  40. Uyar, A., Karapinar, R.: Investigating the precision of Web image search engines for popular and less popular entities. J. Inf. Sci. 43(3), 378–392 (2017)

    Article  Google Scholar 

  41. Wu, L., Wang, Y., Gao, J.B., Li, X.: Deep adaptive feature embedding with local sample distributions for person re-identification. Pattern Recognit. 73, 275–288 (2018)

    Article  Google Scholar 

  42. Yann, L.C., Yoshua, B., Geoffrey, H.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  43. Zhang, H.J., Wang, S., Cao, X., Yue, H., Wang, K.: Learning to link human objects in videos and advertisements with clothes retrieval. In: 2016 International Joint Conference on Neural Networks. pp. 5006–5013 (2016)

  44. Zhang, W.L., Li, R.J., Deng, H.T., Wang, L., Lin, W.L., Ji, S.W., Shen, D.G.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)

    Article  Google Scholar 

  45. Zhang, H.J., Cao, X., Ho, J.K.L., Chow, S.: Object-level video advertising: an optimization framework. IEEE Trans. Ind. Inform. 13(2), 520–531 (2017)

    Article  Google Scholar 

  46. Zhang, J.M., Ma, S.G., Sameki, M., Sclaroff, S., Betke, M., Lin, Z., Shen, X.H., Price, B., Měch, R.: Salient object subitizing. Int. J. Comput. Vis. 124(2), 169–186 (2017)

    Article  MathSciNet  Google Scholar 

  47. Zhou, X.Y., Gong, W., Fu, W.L., Du, F.T.: Application of deep learning in object detection. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science. pp. 631–634 (2017)

  48. Zhuo, T.: Face recognition from a single image per person using deep architecture neural networks. Clust. Comput. 19(1), 73–77 (2016)

    Article  Google Scholar 

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Acknowledgements

Thanks for the great support from Chengdu Handsight Information Technology Co., Ltd. The company offers all the empirical data, as well as the using authorization of IPTV supporting system. Our work is supported by Key support projects of Sichuan science and Technology Department (No.18ZDYF1707), Key support projects of Sichuan Federation of Social Science Associations (No. SC16XK033) and Key support projects of Sichuan Tourism University (No. SCTUJ1709).

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Authors and Affiliations

  1. Business School of Sichuan University, Chengdu, Sichuan, China

    Cheng Luo & Tingting Zhu

  2. Digital Media Art Department, Sichuan Film and Television University, Chengdu, China

    Ying Peng

  3. Research Center for Smart Tourism Technology Application and Innovation, Sichuan Tourism University, PO Box 610100, Chengdu, China

    Ling Li

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  1. Cheng Luo
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Correspondence to Ling Li.

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Luo, C., Peng, Y., Zhu, T. et al. An optimization framework of video advertising: using deep learning algorithm based on global image information. Cluster Comput 22 (Suppl 4), 8939–8951 (2019). https://doi.org/10.1007/s10586-018-2024-3

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