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Facebook5k: A Novel Evaluation Resource Dataset for Cross-Media Search

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11063))

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Abstract

Semantic concepts selection for model construction and data collection is an open research question. It is highly demanding to choose good multimedia concepts with small semantic gaps to facilitate the work of cross-media system developers. Since, this work is very scarce therefore; this paper contributes a new real-world web image dataset created by NGN Tsinghua Laboratory students for cross media search. Unlike previous datasets, such as Flicker30k, Wikipedia and NUS have high semantic gap, results in leading to inconsistency with real time applications. To overcome these drawbacks, the proposed Facebook5k dataset includes: (1) 5130 images crawled from Facebook through users feelings; (2) Images are categorized according to users feelings; (3) Facebook5k is independent of tags and language, rather than uses feelings for search. Based on the proposed dataset, we point out key features of social website images and identify some research problems on image annotation and retrieval. The benchmark results show the effectiveness of the proposed dataset to simplify and improve general image retrieval.

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Notes

  1. 1.

    facebook.com.

  2. 2.

    instagram.com.

  3. 3.

    flickr.com.

  4. 4.

    http://ngn.ee.tsinghua.edu.cn/.

References

  1. Hwang, S.J., Grauman, K.: Reading between the lines: object localization using implicit cues from image tags. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1145–1158 (2012)

    Article  Google Scholar 

  2. Rasiwasia, N., Costa Pereira, J., Coviello, E., Doyle, G., Lanckriet, G.R., Levy, R., Vasconcelos, N.: A new approach to cross-modal multimedia retrieva. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 251–260 (2010)

    Google Scholar 

  3. Grubinger, M., Clough, P., Müller, H., Deselaers, T: The IAPR TC-12 benchmark: a new evaluation resource for visual information systems. In: International Workshop Ontoimage, vol. 5 (2006)

    Google Scholar 

  4. Li, J., Wang, J.Z.: Real-time computerized annotation of pictures. IEEE Trans. Pattern Anal. Mach. Intell. 30, 985–1002 (2008)

    Article  Google Scholar 

  5. Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29, 394–410 (2007)

    Article  Google Scholar 

  6. Von Ahn, L., Dabbish, L: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 319–326. ACM (2004)

    Google Scholar 

  7. Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77, 157–173 (2008)

    Article  Google Scholar 

  8. Wang, X.-J., Zhang, L., Jing, F., Ma, W.-Y.: Annosearch: image auto-annotation by search. In: IEEE computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1483–1490. IEEE Press, New York (2006)

    Google Scholar 

  9. Lu, Y., Zhang, L., Tian, Q., Ma, W.-Y.: What are the high-level concepts with small semantic gaps? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Press, New York (2008)

    Google Scholar 

  10. Peng, Y., Huang, X., Zhao, Y.: An overview of cross-media retrieval: Concepts, methodologies, benchmarks and challenges. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2372–2385 (2018)

    Article  Google Scholar 

  11. Tang, J., Song, Y., Hua, X.-S., Mei, T., Wu, X.: To construct optimal training set for video annotation. In: Proceedings of the 14th ACM International Conference on Multimedia, pp. 89–92. ACM (2006)

    Google Scholar 

  12. Hu, Y., Zheng, L., Yang, Y., Huang, Y.: Twitter100k: a real-world dataset for weakly supervised cross-media retrieval. IEEE Trans. Multimed. 20, 927–938 (2017)

    Article  Google Scholar 

  13. Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. J. Mach. Learn. Res. 3, 1107–1135 (2003)

    MATH  Google Scholar 

  14. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106, 59–70 (2007)

    Article  Google Scholar 

  15. Naphade, M., et al.: Large-scale concept ontology for multimedia. IEEE Multimed. 13, 86–91 (2006)

    Article  Google Scholar 

  16. Snoek, C.G.M., Worring, M., Van Gemert, J.C., Geusebroek, J.-M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of the 14th ACM International Conference on Multimedia, pp. 421–430. ACM Press (2006)

    Google Scholar 

  17. Lu, Y.-J., Nguyen, P.A., Zhang, H., Ngo, C.-W.: Concept-based interactive search system. In: Amsaleg, L., Guðmundsson, G.Þ., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 463–468. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51814-5_42

    Chapter  Google Scholar 

  18. Kambau, R.A., Hasibuan, Z.A.: Concept-based multimedia information retrieval system using ontology search in cultural heritage. In: Second International Conference on Informatics and Computing (ICIC), pp. 1–6. IEEE Press, New York (2017)

    Google Scholar 

  19. Kambau, R.A., Hasibuan, Z.A.: Evolution of information retrieval system: critical review of multimedia information retrieval system based on content, context, and concept. In: 11th International Conference on Information & Communication Technology and System (ICTS), pp. 91–98. IEEE Press, New York (2017)

    Google Scholar 

  20. Li, X., Uricchio, T., Ballan, L., Bertini, M., Snoek, C.G.M., Bimbo, A.D.: Socializing the semantic gap: a comparative survey on image tag assignment, refinement, and retrieval. ACM Comput. Surv. (CSUR) 49 (2016)

    Article  Google Scholar 

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (No. U1405254, U1536115, U1536207).

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Authors and Affiliations

  1. Tsinghua National Laboratory for Information Science and Technology, Beijing, China

    Sadaqat ur Rehman & Yongfeng Huang

  2. Beijing University of Technology, Beijing, China

    Shanshan Tu

  3. Sarhad University of Science and IT, Peshawar, Pakistan

    Obaid ur Rehman

Authors
  1. Sadaqat ur Rehman
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  2. Yongfeng Huang
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  3. Shanshan Tu
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  4. Obaid ur Rehman
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Corresponding author

Correspondence to Shanshan Tu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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ur Rehman, S., Huang, Y., Tu, S., ur Rehman, O. (2018). Facebook5k: A Novel Evaluation Resource Dataset for Cross-Media Search. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_47

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  • DOI: https://doi.org/10.1007/978-3-030-00006-6_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00005-9

  • Online ISBN: 978-3-030-00006-6

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