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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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
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
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
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)
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)
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)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Rujikietgumjorn, S., Watcharapinchai, N., Marukatat, S. (2018). Sloth Search System. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-73600-6_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73599-3
Online ISBN: 978-3-319-73600-6
eBook Packages: Computer ScienceComputer Science (R0)