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A survey on description and modeling of audiovisual documents

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Abstract

The number of audiovisual documents available on the web is exponentially increasing due to the rise of the number of videos produced every day. The recent progress in audiovisual documents field has made it possible to popularize the exchange of these documents in many domains. More generally, the interest in the indexing potential of audiovisual documents has significantly increased in different disciplines, namely films, sports events, etc. Within this framework, several research studies focused on implementing this indexation based on the segmentation of the audiovisual document in fragments. This segmentation was brought by the appropriate descriptions. Although the indexing process seems essential, the way of exploiting and searching audiovisual documents remains unsatisfactory. Indeed, annotations based on generic descriptions (title, creator, publisher, etc.) are insufficient to describe the content of the audiovisual documents. With the proliferation of audiovisual documents and the mentioned indexing limits, the question that should be answered is: “What is the relevant information of the audiovisual content?”. In this paper, we present a survey to characterize the description and the modeling of audiovisual documents. We classify the existing description methods into three categories: a low-level description, a documentary description and a semantic description. The main objective of this study is to propose an approach that helps describe and organize the content of an audiovisual document so as to conduct a better inquiry.

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  1. http://www.nist.gov/itl/iad/mig/med11.cfm

  2. http://dublincore.org/

References

  1. AE Abduraman, SA Berrani, and B Merialdo (2012). “TV Program Structuring Techniques,” TV Content Anal. Tech. Appl., p. 157

  2. S Antol, A Agrawal, J Lu, M Mitchell, D Batra, C Lawrence Zitnick, and D Parikh (2015). “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision, pp. 2425–2433

  3. D Arthur and S Vassilvitskii (2007), “k-means++: The advantages of careful seeding,” in Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 1027–1035

  4. B Bachimont (1994). “Le Contrôle Dans les Systèmes À Base de Connaissances Contribution À l’Épistémologie de l'Intelligence Artificielle”

  5. Ballan L, Bertini M, Del Bimbo A, Seidenari L, Serra G (2011) Event detection and recognition for semantic annotation of video. Multimed Tools Appl 51(1):279–302

    Article  Google Scholar 

  6. S Banerjee and A Lavie (2005). “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp. 65–72

  7. Bhardwaj RK, Margam M (2017) Metadata framework for online legal information system in indian environment. Libr Rev 66(1/2):49–68

    Article  Google Scholar 

  8. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    MATH  Google Scholar 

  9. Burghouts GJ, Geusebroek J-M (2009) Performance evaluation of local colour invariants. Comput Vis Image Underst 113(1):48–62

    Article  Google Scholar 

  10. Caillet M, Roisin C, Carrive J (2014) Multimedia applications for playing with digitized theater performances. Multimed Tools Appl 73(3):1777–1793

    Article  Google Scholar 

  11. X Chang, Y Yang, A Hauptmann, EP Xing, and YL Yu 2015. “Semantic concept discovery for large-scale zero-shot event detection,” in Twenty-fourth international joint conference on artificial intelligence

  12. M Chen and A Hauptmann (1995). “Mosift: recognizing human actions in surveillance videos,”

  13. Chuttur MY (2014) Investigating the effect of definitions and best practice guidelines on errors in Dublin Core metadata records. J Inf Sci 40(1):28–37

    Article  Google Scholar 

  14. N Dalal, B Triggs, and C Schmid (2006). “Human detection using oriented histograms of flow and appearance,” in European conference on computer vision, pp. 428–441

  15. Dasiopoulou S, Tzouvaras V, Kompatsiaris I, Strintzis MG (2010) Enquiring MPEG-7 based multimedia ontologies. Multimed Tools Appl 46(2–3):331–370

    Article  Google Scholar 

  16. Z De Linde and N Kay (2016). The semiotics of subtitling. Routledge

  17. Del Fabro M, Böszörmenyi L (2013) State-of-the-art and future challenges in video scene detection: a survey. Multimedia Systems 19(5):427–454

    Article  Google Scholar 

  18. Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, Quadrana M (2016) Content-based video recommendation system based on stylistic visual features. J Data Semant 5(2):99–113

    Article  Google Scholar 

  19. Deldjoo Y, Elahi M, Quadrana M, Cremonesi P (2018) Using visual features based on MPEG-7 and deep learning for movie recommendation. Int J Multimed Inf Retr 7(4):207–219

    Article  Google Scholar 

  20. B Dervin (1992). “From the mind’s eye of the user: the sense-making qualitative-quantitative methodology,” Sense-making Methodol. Read

  21. E Egyed-Zsigmond, Y Prié, A Mille, and JM Pinon (2000). “A graph based audio-visual document annotation and browsing system,” in Content-Based Multimedia Information Access-Volume 2, pp. 1381–1389

  22. Elleuch N, Ben Ammar A, Alimi A (2015) A generic framework for semantic video indexing based on visual concepts/contexts detection. Multimed Tools Appl 74(4):1397–1421

    Article  Google Scholar 

  23. Fang Z, Liu J, Li Y, Qiao Y, Lu H (2019) Improving visual question answering using dropout and enhanced question encoder. Pattern Recogn 90:404–414

    Article  Google Scholar 

  24. Fourati M, Jedidi A, Ben Hassin H, Gargouri F (2015) Towards fusion of textual and visual modalities for describing audiovisual documents. Int J Multimed Data Eng Manag 6(2):52–70

    Article  Google Scholar 

  25. Fourati M, Jedidi A, Gargouri F (2015) Topic and Thematic Description for Movies Documents. In: Arik S, Huang T, Lai WK, Liu Q (eds) Neural Information Processing SE - 54, vol. 9492. Springer International Publishing, pp 453–462

  26. Z Gan, C Gan, X He, Y Pu, K Tran, J Gao, L Carin, and L Deng (2017). “Semantic compositional networks for visual captioning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5630–5639

  27. Gao L, Guo Z, Zhang H, Xu X, Shen HT (2017) Video captioning with attention-based LSTM and semantic consistency. IEEE Trans Multimed 19(9):2045–2055

    Article  Google Scholar 

  28. M Gluck (1997). “Making sense of semiotics: privileging respondents in revealing contextual geographic syntactic and semantic codes,” in Proceedings of an international conference on Information seeking in context, pp. 53–66

  29. A Holzinger, G Searle, A Auinger, and M Ziefle (2011). “Informatics as Semiotics Engineering: Lessons Learned from Design, Development and Evaluation of Ambient Assisted Living Applications for Elderly People BT - Universal Access in Human-Computer Interaction. Context Diversity,”, pp. 183–192

  30. NJ Janwe and KK Bhoyar (2013). “Video shot boundary detection based on JND color histogram,” in 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pp. 476–480

  31. Jedidi A (2005) Modélisation générique de documents multimédia par des métadonnées: mécanismes d’annotation et d'interrogation. Université Paul Sabatier-Toulouse III

  32. Jiang Y-G, Yang J, Ngo C-W, Hauptmann AG (2009) Representations of keypoint-based semantic concept detection: a comprehensive study. IEEE Trans Multimed 12(1):42–53

    Article  Google Scholar 

  33. S Kim, H Hong, and J Nang (2015). “A Gradual Shot Change Detection using Combination of Luminance and Motion Features for Frame Rate Up Conversion,” in 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 295–299

  34. Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2–3):107–123

    Article  Google Scholar 

  35. Li L, Tang S, Zhang Y, Deng L, Tian Q (2017) Gla: global–local attention for image description. IEEE Trans Multimed 20(3):726–737

    Article  Google Scholar 

  36. Z Liu (2013). “A semiotic interpretation of sense-making in information seeking,” Libr. Philos. Pract., p. 1

  37. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  38. Lu Z-M, Shi Y (2013) Fast video shot boundary detection based on SVD and pattern matching. IEEE Trans Image Process 22(12):5136–5145

    Article  MathSciNet  Google Scholar 

  39. Luo B, Li H, Meng F, Wu Q, Huang C (2017) Video object segmentation via global consistency aware query strategy. IEEE Trans Multimed 19(7):1482–1493

    Article  Google Scholar 

  40. I Mademlis, N Nikolaidis, and I Pitas (2015). “Stereoscopic video description for key-frame extraction in movie summarization,” in 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 819–823

  41. JP Martin (2005). “Description sémiotique de contenus audiovisuels,” Université de Paris-Sud. Faculté des Sciences d’Orsay (Essonne)

  42. P Mickan and E Lopez (2016). Text-based research and teaching: a social semiotic perspective on language in use. Springer

  43. Mingers J, Willcocks L (2017) An integrative semiotic methodology for IS research. Inf Organ 27(1):17–36

    Article  Google Scholar 

  44. Morris RCT (1994) Toward a user-centered information service. J Am Soc Inf Sci 45(1):20–30

    Article  Google Scholar 

  45. Naphade M, Smith JR, Tesic J, Chang S-F, Hsu W, Kennedy L, Hauptmann A, Curtis J (2006) Large-scale concept ontology for multimedia. IEEE Multimed 13(3):86–91

    Article  Google Scholar 

  46. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  47. F Orlandi, J Debattista, IA Hassan, C Conran, M Latifi, M Nicholson, FA Salim, D Turner, O Conlan, and D O’sullivan (2018). “Leveraging Knowledge Graphs of Movies and Their Content for Web-Scale Analysis,” in 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 609–616

  48. K Papineni, S Roukos, T Ward, and WJ Zhu (2002). “BLEU: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting on association for computational linguistics, pp. 311–318

  49. Patel U, Shah P, Panchal P (2013) Shot detection using pixel wise difference with adaptive threshold and color histogram method in compressed and uncompressed video. Int J Comput Appl 64(4):38–44

    Google Scholar 

  50. Peirce CS (2009) Writings of Charles S. Peirce: A Chronological Edition, Volume 8: 1890–1892, vol. 8. Indiana University Press

  51. Poli J-P (2008) An automatic television stream structuring system for television archives holders. Multimedia Systems 14(5):255–275

    Article  Google Scholar 

  52. S Ren, K He, R Girshick, and J Sun (2015). “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Adv Neural Inf Proces Syst, pp. 91–99

  53. Rinaldi AM (2014) A multimedia ontology model based on linguistic properties and audio-visual features. Inf. Sci. (Ny). 277:234–246

    Article  Google Scholar 

  54. LA Rowe, JS Boreczky, and CA Eads (1994). “Indexes for user access to large video databases,” in IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, pp. 150–161

  55. Sánchez-Nielsen E, Chávez-Gutiérrez F, Lorenzo-Navarro J (2019) A semantic parliamentary multimedia approach for retrieval of video clips with content understanding. Multimedia Systems:1–18

  56. Shrivastav S, Kumar S, Kumar K (2017) Towards an ontology based framework for searching multimedia contents on the web. Multimed Tools Appl 76(18):18657–18686

    Article  Google Scholar 

  57. LF Sikos (2017). “The Semantic Gap,” in Description Logics in Multimedia Reasoning, Springer, pp. 51–66

  58. LF Sikos (2018). “Ontology-based structured video annotation for content-based video retrieval via spatiotemporal reasoning,” in Bridging the Semantic Gap in Image and Video Analysis, Springer, pp. 97–122

  59. LF Sikos and DMW Powers (2015). “Knowledge-driven video information retrieval with LOD: from semi-structured to structured video metadata,” in Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, pp. 35–37

  60. Smeaton AF, Over P, Doherty AR (2010) Video shot boundary detection: seven years of TRECVid activity. Comput Vis Image Underst 114(4):411–418

    Article  Google Scholar 

  61. Song J, Gao L, Liu L, Zhu X, Sebe N (2018) Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recogn 75:175–187

    Article  Google Scholar 

  62. Song J, Gao L, Nie F, Shen HT, Yan Y, Sebe N (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Trans Image Process 25(11):4999–5011

    Article  MathSciNet  MATH  Google Scholar 

  63. J Song, Y Guo, L Gao, X Li, A Hanjalic, and HT Shen (2018). “From deterministic to generative: multimodal stochastic RNNs for video captioning,” IEEE Trans. neural networks Learn. Syst

  64. Song J, Zhang H, Li X, Gao L, Wang M, Hong R (2018) Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans Image Process 27(7):3210–3221

    Article  MathSciNet  MATH  Google Scholar 

  65. P Stockinger (2003). “Le document audiovisuel,” Hermes, Lavoisier

  66. P Stockinger (2011). Les archives audiovisuelles : description, indexation et publication. Lavoisier

  67. Stockinger P (2013) Audiovisual archives: digital text and discourse analysis. John Wiley & Sons

  68. A Tamrakar, S Ali, Q Yu, J Liu, O Javed, A Divakaran, H Cheng, and H Sawhney (2012). “Evaluation of low-level features and their combinations for complex event detection in open source videos,” in 2012 IEEE Conference on Computer Vision and Pattern Recogn, pp. 3681–3688

  69. Tang P, Wang C, Wang X, Liu W, Zeng W, Wang J (2019) Object detection in videos by high quality object linking. IEEE Trans. Pattern Anal. Mach. Intell

  70. R Vedantam, C Lawrence Zitnick, and D Parikh (2015). “Cider: Consensus-based image description evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4566–4575

  71. Wang X, Gao L, Song J, Shen H (2016) Beyond frame-level CNN: saliency-aware 3-D CNN with LSTM for video action recognition. IEEE Signal Process Lett 24(4):510–514

    Article  Google Scholar 

  72. Wang X, Gao L, Wang P, Sun X, Liu X (2017) Two-stream 3-d convnet fusion for action recognition in videos with arbitrary size and length. IEEE Trans Multimed 20(3):634–644

    Article  Google Scholar 

  73. W Wang, J Shen, and F Porikli (2015). “Saliency-aware geodesic video object segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3395–3402

  74. Wu Q, Teney D, Wang P, Shen C, Dick A, van den Hengel A (2017) Visual question answering: a survey of methods and datasets. Comput Vis Image Underst 163:21–40

    Article  Google Scholar 

  75. Xu Z, Hu C, Mei L (2016) Video structured description technology based intelligence analysis of surveillance videos for public security applications. Multimed Tools Appl 75(19):12155–12172

    Article  Google Scholar 

  76. Z Xu, F Zhi, C Liang, M Lin, and X Luo (2014). “Semantic annotation of traffic video resources,” in 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, pp. 323–328

  77. Yasser CM (2011) An analysis of problems in metadata records. J Libr Metadata 11(2):51–62

    Article  Google Scholar 

  78. G Ye, Y Li, H Xu, D Liu, and SF Chang (2015). “Eventnet: A large scale structured concept library for complex event detection in video,” in Proceedings of the 23rd ACM international conference on Multimedia, pp. 471–480

  79. Q You, H Jin, Z Wang, C Fang, and J Luo (2016). “Image captioning with semantic attention,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4651–4659

  80. W Zhou, H Li, and Q Tian (2017). “Recent advance in content-based image retrieval: A literature survey,” arXiv Prepr. arXiv1706.06064

  81. Zlitni T, Bouaziz B, Mahdi W (2016) Automatic topics segmentation for TV news video using prior knowledge. Multimed Tools Appl 75(10):5645–5672

    Article  Google Scholar 

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Fourati, M., Jedidi, A. & Gargouri, F. A survey on description and modeling of audiovisual documents. Multimed Tools Appl 79, 33519–33546 (2020). https://doi.org/10.1007/s11042-020-09589-9

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