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Utilizing Sensor-Social Cues to Localize Objects-of-Interest in Outdoor UGVs

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9516))

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Abstract

A huge number of outdoor user-generated videos (UGVs) are recorded daily due to the popularity of mobile intelligent devices. Managing these videos is a tough challenge in multimedia field. In this paper, we tackle this problem by performing object-of-interest (OOI) recognition in UGVs to identify semantically important regions. By leveraging geo-sensor and social data, we propose a novel framework for OOI recognition in outdoor UGVs. Firstly, the OOI acquisition is conducted to obtain an OOI frame set from UGVs. Simultaneously, the classified object set recommendation is performed to obtain a candidate category name set from social networks. Afterward, a spatial pyramid representation is deployed to describe social objects from images and OOIs from UGVs, respectively. Finally, OOIs with their annotated names are labeled in UGVs. Extensive experiments in outdoor UGVs from both Nanjing and Singapore demonstrated the competitiveness of our approach.

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Notes

  1. 1.

    https://foursquare.com/.

  2. 2.

    www.geovid.org.

References

  1. Zheng, Y.-T., Zhao, M., Song, Y., Adam, H.: Tour the world: building a web-scale landmark recognition engine. In: Proceedings of CVPR (2009)

    Google Scholar 

  2. Hao, J., Wang, G., Seo, B., Zimmermann, R.: Point of interest detection and visual distance estimation for sensor-rich video. IEEE T-MM 16(7), 1929–1941 (2014)

    Google Scholar 

  3. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR (2006)

    Google Scholar 

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of CVPR (2005)

    Google Scholar 

  5. Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE T-PAMI 32(9), 1627–1645 (2010)

    Article  Google Scholar 

  6. Yang, K., Wang, M., Hua, X.-S., Yan, S., Zhang, H.-J.: Assemble new object detector with few examples. IEEE T-IP 20(12), 3341–3349 (2011)

    Article  MathSciNet  Google Scholar 

  7. Wang, M., Hua, X.-S., Hong, R., Tang, J., Qi, G.-J., Song, Y.: Unified video annotation via multi-graph learning. IEEE T-CSVT 19(5), 733–746 (2009)

    Google Scholar 

  8. Harzallah, H., Jurie, F., Schmid, C.: Combining efficient object localization and image classification. In: Proceedings of ICCV (2009)

    Google Scholar 

  9. Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: Proceedings of ICCV (2009)

    Google Scholar 

  10. Cinbis, R.G., Verbeek, J.J., Schmid, C.: Segmentation driven object detection with fisher vectors. In: Proceedings of ICCV (2013)

    Google Scholar 

  11. Kim, S., Park, S., Kim, M.: Central object extraction for object-based image retrieval. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 39–49. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Zhang, D., Javed, O., Shah, M.: Video object segmentation through spatially accurate and temporally dense extraction of primary object regions. In: Proceedings of CIVR (2013)

    Google Scholar 

  13. Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N.: Automatic salient object segmentation based on context and shape prior. In: Proceedings of BMVC (2011)

    Google Scholar 

  14. Khuwuthyakorn, P., Robles-Kelly, A., Zhou, J.: Object of interest detection by saliency learning. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 636–649. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: Proceedings of CVPR (2013)

    Google Scholar 

  16. Rosin, P.L.: A simple method for detecting salient regions. Pattern Recogn. 42(11), 2363–2371 (2009)

    Article  MATH  Google Scholar 

  17. Jia, Y., Han, M.: Category-independent object-level saliency detection. In: Proceedings of ICCV (2013)

    Google Scholar 

  18. Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by UFO: uniqueness, focusness and objectness. In: Proceedings of ICCV (2013)

    Google Scholar 

  19. Navalpakkam, V., Itti, L.: Modeling the influence of task on attention. Vision. Res. 45(2), 205–231 (2005)

    Article  Google Scholar 

  20. Borji, A.: Boosting bottom-up and top-down visual features for saliency estimation. In: Proceedings of CVPR (2012)

    Google Scholar 

  21. Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing Networks and Markov Chains, 2nd edn. John Wiley, Hoboken (2006)

    Book  MATH  Google Scholar 

  22. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE T-PAMI 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  23. Zhang, L., Bian, W., Song, M., Tao, D., Liu, X.: Integrating local features into discriminative graphlets for scene classification. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part III. LNCS, vol. 7064, pp. 657–666. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  24. Zhang, L., Song, M., Sun, L., Liu, X., Wang, Y., Tao, D., Bu, J., Chen, C.: Spatial graphlet matching kernel for recognizing aerial image categories. In: ICPR (2012)

    Google Scholar 

  25. Zhang, L., Gao, Y., Zimmermann, R., Tian, Q., Li, X.: Fusion of multichannel local and global structural cues for photo aesthetics evaluation. IEEE T-IP 23(3), 1419–1429 (2014)

    Article  MathSciNet  Google Scholar 

  26. Zhang, L., Wang, M., Nie, L., Hong, L., Rui, Y., Tian, Q.: Retargeting semantically-rich photos. IEEE T-MM 17(9), 1538–1549 (2015)

    Google Scholar 

  27. Zhang, L., Gao, Y., Hong, R., Hu, Y., Ji, R., Dai, Q.: Probabilistic skimlets fusion for summarizing multiple consumer landmark videos. IEEE T-MM 17(1), 40–49 (2015)

    Google Scholar 

  28. Ay, S.A., Zimmermann, R., Kim, S.H.: Viewable scene modeling for geospatial video search. In: ACM Multimedia (2008)

    Google Scholar 

  29. Zheng, Y.-T., Zha, Z.-J., Chua, T.-S.: Research and applications on georeferenced multimedia. Multimedia Tools Appl. 51(1), 77–98 (2011)

    Article  Google Scholar 

  30. Rodden, K., Wood, K.R.: How do people manage their digital photographs? In: ACM SIGCHI (2003)

    Google Scholar 

  31. Kentaro, T., Logan, R., Roseway, A., Anandan, P.: Geographic location tags on digital images. In: ACM Multimedia (2003)

    Google Scholar 

  32. Föckler, P., Zeidler, T., Brombach, B., Bruns, E., Bimber, O.: PhoneGuide: museum guidance supported by on-device object recognition on mobile phones. In: Proceedings of Mobile and Ubiquitous Multimedia (2005)

    Google Scholar 

  33. Gammeter, S., Gassmann, A., Bossard, L.: Server-side object recognition and client-side object tracking for mobile augmented reality. In: Proceedings of CVPR (2010)

    Google Scholar 

  34. Wang, M., Gao, Y., Ke, L., Rui, Y.: View-based discriminative probabilistic modeling for 3D object retrieval and recognition. IEEE T-IP 22(4), 1395–1407 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer Sciences, Zhejiang University, Hangzhou, China

    Yingjie Xia

  2. Department of CSIE, Hefei University of Technology, Hefei, China

    Luming Zhang

  3. School of Computing, National University of Singapore, Singapore, Singapore

    Liqiang Nie & Wenjing Geng

Authors
  1. Yingjie Xia
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  2. Luming Zhang
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  3. Liqiang Nie
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  4. Wenjing Geng
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Corresponding author

Correspondence to Luming Zhang .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Xia, Y., Zhang, L., Nie, L., Geng, W. (2016). Utilizing Sensor-Social Cues to Localize Objects-of-Interest in Outdoor UGVs. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-27671-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27670-0

  • Online ISBN: 978-3-319-27671-7

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