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On the use of commonsense ontology for multimedia event recounting

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International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

Textually narrating the observed evidences relevant to the reasons why a video clip is being retrieved for an event is still a highly challenging problem. This paper explores the use of a commonsense ontology, namely ConceptNet, in generating short descriptions for recounting the audio–visual evidences. The ontology is exploited as a knowledge engine to provide event–relevant common sense, which is expressed in terms of concepts and their relationships, for semantics understanding, context-based concept screening and sentence synthesis. A principal way of exploiting the ontology, from extracting the event–relevant semantic network to the formation of syntactic parse trees, is outlined and discussed. Experimental results on two benchmark datasets (TRECVID MED and MediaEval) show the effectiveness of our approach. The findings show insights on the usability of common sense for multimedia search, including the feasibility of inferring relevant concepts for event detection, as well as the quality of textual sentences in meeting human expectation.

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Notes

  1. https://www.youtube.com/yt/press/statistics.html.

  2. Refer to http://vireo.cs.cityu.edu.hk/mer_demo/networks.html for twenty event networks generated for TRECVID MED 2012.

  3. -ing and -s are omitted in ConceptNet.

References

  1. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239

    Article  Google Scholar 

  2. Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-wide: a real-world web image database from National University of Singapore. In: Proceedings of CIVR, pp 48:1–48:9

  3. Cilibrasi RL, Vitanyi PMB (2007) The Google similarity distance. IEEE Trans Knowl Data Eng 19(3):370–383

    Article  Google Scholar 

  4. Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: Proceedings of ECCV, pp 428–441

  5. Das P, Xu C, Doell RF, Corso JJ (2013) A thousand frames in just a few words: lingual description of videos through latent topics and sparse object stitching. In: Proceedings of CVPR, pp 2634–2641

  6. Demarty CH, Penet C, Schedl M, Ionescu B, Quang VL, Jiang YG (2013) The MediaEval 2013 affect task: violent scenes detection. In: MediaEval workshop

  7. Deng J, Dong W, Socher R, Jia Li L, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: Proceedings of CVPR

  8. Ding D, Metze F, Rawat S, Schulam PF, Burger S, Younessian E, Bao L, Christel MG, Hauptmann A (2012) Beyond audio and video retrieval: towards multimedia summarization. In: Proceedings of ICMR, pp 2:1–2:8

  9. Farhadi A, Hejrati SMM, Sadeghi MA, Young P, Rashtchian C, Hockenmaier J, Forsyth DA (2010) Every picture tells a story: generating sentences from images. In: Proceedings of ECCV, pp 15–29

  10. Guadarrama S, Krishnamoorthy N, Malkarnenkar G, Venugopalan S, Mooney RJ, Darrell T, Saenko K (2013) YouTube2Text: recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition. In: Proceedings of ICCV, pp 2712–2719

  11. Gupta A, Verma Y, Jawahar CV (2012) Choosing linguistics over vision to describe images. In: Proceedings of AAAI

  12. Izadinia H, Shah M (2012) Recognizing complex events using large margin joint low-level event model. In: Proceedings of ECCV, pp 430–444

  13. Jiang YG, Dai Q, Wang J, Ngo CW, Xue X, Chang SF (2012) Fast semantic diffusion for large scale context-based image and video annotation. IEEE Trans Image Process 21(6):3080–3091

    Article  MathSciNet  Google Scholar 

  14. Jiang YG, Ngo CW, Chang SF (2009) Semantic context transfer across heterogeneous sources for domain adaptive video search. In: Proceedings of ACM MM, pp 155–164

  15. Jiang YG, Ye G, Chang SF, Ellis D, Loui AC (2011) Consumer video understanding: a benchmark database and an evaluation of human and machine performance. In: Proceedings of ICMR

  16. Khan MUG, Zhang L, Gotoh Y (2011) Towards coherent natural language description of video streams. In: ICCV workshops, pp 664–671

  17. Krishnamoorthy N, Malkarnenkar G, Mooney RJ, Saenko K, Guadarrama S (2013) Generating natural-language video descriptions using text-mined knowledge. In: Proceedings of AAAI

  18. Kulkarni G, Premraj V, Dhar S, Li S, Choi Y, Berg AC, Berg TL (2011) Baby talk: understanding and generating simple image descriptions. In: Proceedings of CVPR, pp 1601–1608

  19. Kuznetsova P, Ordonez V, Berg AC, Berg TL, Choi Y (2012) Collective generation of natural image descriptions. In: Proceedings of ACL, pp 359–368

  20. Li S, Kulkarni G, Berg TL, Berg AC, Choi Y (2011) Composing simple image descriptions using web-scale n-grams. In: Proceedings of CoNLL, pp 220–228

  21. Lin Y, Michel JB, Aiden EL, Orwant J, Brockman W, Petrov S (2012) Syntactic annotations for the google books n-gram corpus. In: Proceedings of ACL, pp 169–174

  22. Liu H, Singh P (2004) Conceptnet—a practical commonsense reasoning tool-kit. BT Technol J 22(4):211–226

    Article  MathSciNet  Google Scholar 

  23. Liu J, Yu Q, Javed O, Ali S, Tamrakar A, Divakaran A, Cheng H, Sawhney HS (2013) Video event recognition using concept attributes. In: Proceedings of WACV, pp 339–346

  24. Ma Z, Hauptmann AG, Yang Y, Sebe N (2012) Classifier-specific intermediate representation for multimedia tasks. In: Proceedings of ICMR, pp 50:1–50:8

  25. Marszałek M, Laptev I, Schmid C (2009) Actions in context. In: Proceedings of CVPR, pp 2929–2936

  26. Mathieu B, Essid S, Fillon T, Prado J, Richard G (2010) Yaafe, an easy to use and efficient audio feature extraction software. In: Downie JS, Veltkamp RC (eds) Proceedings of ISMIR, pp 441–446

  27. Mazloom M, Gavves E, van de Sande KEA, Snoek C (2013) Searching informative concept banks for video event detection. In: Proceedings of ICMR, pp 255–262

  28. Merler M, Huang B, Xie L, Hua G, Natsev A (2012) Semantic model vectors for complex video event recognition. IEEE Trans Multimed 14(1):88–101

    Article  Google Scholar 

  29. Mitchell M, Han X, Dodge J, Mensch A, Goyal A, Berg A, Yamaguchi K, Berg T, Stratos K, Daumé III H (2012) Midge: generating image descriptions from computer vision detections. In: Proceedings of EACL, pp 747–756

  30. Natarajan P, Wu S, Luisier F, Zhuang X, Tickoo M, Ye G, Liu D, Chang SF, Saleemi I, Shah M, Davis L, Gupta A, Haritaoglu I, Guler S, Morde A (2013) BBN VISER TRECVID 2013 multimedia event detection and multimedia event recounting systems. In: NIST TRECVID workshop

  31. Nie F, Huang Y, Wang X, Huang H (2014) New primal SVM solver with linear computational cost for big data classifications. In: Proceedings of ICML

  32. NIST, Information Technology Laboratory: 2012 TRECVID Multimedia Event Detection Track

  33. NIST, Information Technology Laboratory: 2013 TRECVID Multimedia Event Recounting Track

  34. Ordonez V, Kulkarni G, Berg TL (2011) Im2Text: describing images using 1 million captioned photographs. In: Proceedings of NIPS, pp 1143–1151

  35. Rohrbach M, Qiu W, Titov I, Thater S, Pinkal M, Schiele B (2013) Translating video content to natural language descriptions. In: Proceedings of ICCV, pp 433–440

  36. Romano J (1990) On the behavior of randomization tests without a group invariance assumption. J Am Stat Assoc 85(411):686–692

    Article  MathSciNet  MATH  Google Scholar 

  37. Snoek CGM, Worring M, van Gemert JC, Geusebroek JM, Smeulders AWM (2006) The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of ACM MM, pp 421–430

  38. Speer R, Havasi C, Lieberman H (2008) Analogyspace: reducing the dimensionality of common sense knowledge. In: Proceedings of AAAI, pp 548–553

  39. Sun C, Burns B, Nevatia R, Snoek CGM, Bolles B, Myers GK, Wang W, Yeh E (2014) Isomer: informative segment observations for multimedia event recounting. In: Proceedings of ICMR

  40. Tan CC, Jiang YG, Ngo CW (2011) Towards textually describing complex video contents with audio–visual concept classifiers. In: Proceedings of ACM MM, pp 655–658

  41. Tan CC, Ngo CW (2013) The vireo team at MediaEval 2013: violent scenes detection by mid-level concepts learnt from youtube. In: MediaEval, Proceedings of CEUR workshop, vol 1043

  42. Torralba A, Murphy KP, Freeman WT (2010) Using the forest to see the trees: exploiting context for visual object detection and localization. Commun ACM 53(3):107–114

    Article  Google Scholar 

  43. Verma Y, Gupta A, Mannem P, Jawahar CV (2013) Generating image descriptions using semantic similarities in the output space. In: CVPR workshops, pp 288–293

  44. Vinyals O, Toshev A, Bengio S, Erhan D (2014) Show and tell: a neural image caption generator. CoRR

  45. Wang H, Kläser A, Schmid C, Liu CL (2011) Action recognition by dense trajectories. In: Proceedings of CVPR, pp 3169–3176. Colorado Springs, USA

  46. Weng MF, Chuang YY (2012) Cross-domain multicue fusion for concept-based video indexing. IEEE Trans Pattern Anal Mach Intell 34(10):1927–1941

    Article  Google Scholar 

  47. Yanagawa A, Chang SF, Kennedy L, Hsu W (2007) Columbia University’s baseline detectors for 374 LSCOM semantic visual concepts. Technical report, Columbia University

  48. Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723–742

    Article  Google Scholar 

  49. Yang Y, Teo CL, Daumé III H, Aloimonos Y (2011) Corpus-guided sentence generation of natural images. In: Proceedings of EMNLP, pp 444–454

  50. Zhang J, Marszałek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213–238

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Chun-Chet Tan & Chong-Wah Ngo

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  1. Chun-Chet Tan
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  2. Chong-Wah Ngo
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Correspondence to Chun-Chet Tan.

Additional information

The work described in this paper was fully supported by a Grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 120213).

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Tan, CC., Ngo, CW. On the use of commonsense ontology for multimedia event recounting. Int J Multimed Info Retr 5, 73–88 (2016). https://doi.org/10.1007/s13735-015-0090-3

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