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Sloth Search System

  • Sitapa Rujikietgumjorn
  • Nattachai Watcharapinchai
  • Sanparith Marukatat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10705)

Abstract

In this paper, we present the Sloth Search System (SSS) for large scale video browsing. Our key concept is to apply object recognition and scene classification to generate keyword tags from video images. This indexing process is performed only on selected frames for faster processing. The keyword tags are used to retrieve videos from a text-based query. Additional feature signatures are also used to extract spatial and color information. These proposed signatures are stored as binary codes for a compact representation and for fast search. Such a representation allows users to search by drawing a sketch or a bounding box of a specific object.

Keywords

Content-based video retrieval Video search Convolutional neural networks Known Item Search Sketch search 

References

  1. 1.
    Cobârzan, C., Schoeffmann, K., Bailer, W., Hürst, W., Blažek, A., Lokoč, J., Vrochidis, S., Barthel, K.U., Rossetto, L.: Interactive video search tools: a detailed analysis of the video browser showdown 2015. Multimedia Tools Appl. 76(4), 5539–5571 (2017)CrossRefGoogle Scholar
  2. 2.
    Rossetto, L., Giangreco, I., Tănase, C., Schuldt, H., Dupont, S., Seddati, O.: Enhanced retrieval and browsing in the IMOTION system. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 469–474. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-51814-5_43 CrossRefGoogle Scholar
  3. 3.
    Barthel, K.U., Hezel, N., Mackowiak, R.: Navigating a graph of scenes for exploring large video collections. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 418–423. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-27674-8_43 CrossRefGoogle Scholar
  4. 4.
    Blažek, A., Lokoč, J., Matzner, F., Skopal, T.: Enhanced signature-based video browser. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8936, pp. 243–248. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-14442-9_22 Google Scholar
  5. 5.
    Awad, G., Butt, A., Fiscus, J., Joy, D., Delgado, A., Michel, M., Smeaton, A.F., Graham, Y., Kraaij, W., Quénot, G., Eskevich, M., Ordelman, R., Jones, G.J.F., Huet, B.: Evaluating ad-hoc and instance video search, events detection, video captioning and hyperlinking. In: Proceedings of TRECVID 2017, TRECVID 2017. NIST, Gaithersburg (2017)Google Scholar
  6. 6.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems (NIPS) (2015)Google Scholar
  7. 7.
    Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., Murphy, K.: Speed/accuracy trade-offs for modern convolutional object detectors. CoRR abs/1611.10012 (2016)Google Scholar
  8. 8.
    Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2017).  https://doi.org/10.1109/TPAMI.2017.2723009 Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sitapa Rujikietgumjorn
    • 1
  • Nattachai Watcharapinchai
    • 1
  • Sanparith Marukatat
    • 1
  1. 1.National Electronics and Computer Technology Center (NECTEC)Pathum ThaniThailand

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