Abstract
This paper demonstrates VERGE, an interactive video retrieval engine for browsing a collection of images or videos and searching for specific content. The engine integrates a multitude of retrieval methodologies that include visual and textual searches and further capabilities such as fusion and reranking. All search options and results appear in a web application that aims at a friendly user experience.
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
Awad, G., Butt, A., et al.: TRECVID 2018: benchmarking video activity detection, video captioning and matching, video storytelling linking and video search (2018)
Lokoč, J., Kovalčík, G., et al.: Interactive search or sequential browsing? A detailed analysis of the video browser showdown 2018. ACM TOMM 15(1), 29 (2019)
Yang, H.-F., Lin, K., Chen, C.-S.: Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Trans. PAMI 40(2), 437–451 (2017)
Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. PAMI 33(1), 117–128 (2011)
Markatopoulou, F., Moumtzidou, A., Galanopoulos, D., et al.: ITI-CERTH participation in TRECVID 2017. In: Proceedings of TRECVID 2017 Workshop, USA (2017)
Zhou, B., Lapedriza, A., et al.: Places: a 10 million image database for scene recognition. IEEE Trans. PAMI 40(6), 1452–1464 (2017)
Markatopoulou, F., Mezaris, V., Patras, I.: Implicit and explicit concept relations in deep neural networks for multi-label video/image annotation. IEEE Trans. Circuits Syst. Video Technol. 29(6), 1631–1644 (2018)
Guangnan, Y., Yitong, L., Hongliang, X., et al.: EventNet: a large scale structured concept library for complex event detection in video. In Proceedings of ACM MM (2015)
Gu, C., Sun, C., Ross, D.A., et al.: AVA: a video dataset of spatio-temporally localized atomic visual actions. In: Proceedings of the IEEE Conference on CVPR, pp. 6047–6056 (2018)
Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.: Ceci n’est pas une pipe: a deep convolutional network for fine-art paintings classification. In 2016 IEEE ICIP, pp. 3703–3707. IEEE (2016)
Dong, J., Li, X., Xu, C., et al.: Dual encoding for zero-example video retrieval. In: Proceedings of the IEEE Conference on CVPR, pp. 9346–9355 (2019)
Cho, K., Van M.B., Gulcehre, C., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint. arXiv:1406.1078 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint. arXiv:1408.5882 (2014)
Faghri, F., Fleet, D.J., Kiros, J.R., Fidler, S.: VSE++: improving visual-semantic embeddings with hard negatives (2018)
Li, Y., Song, Y., Cao, L., et al.: TGIF: a new dataset and benchmark on animated GIF description. In: The IEEE Conference on CVPR (2016)
Xu, J., Mei, T., Yao, T., Rui, Y.: MSR-VTT: a large video description dataset for bridging video and language. In: The IEEE Conference on CVPR (June 2016)
Lamere, P., Kwok, P., Gouvea, E., et al.: The CMU SPHINX-4 speech recognition system. In: IEEE ICASSP 2003, vol. 1, pp. 2–5, Hong Kong (2003)
Venugopalan, S., Rohrbach, M., Donahue, J., et al.: Sequence to sequence-video to text. In: Proceedings of the IEEE ICCV, pp. 4534–4542 (2015)
Phan, S., Henter, G.E., Miyao, Y., Satoh, S.: Consensus-based sequence training for video captioning. arXiv preprint. arXiv:1712.09532 (2017)
Gialampoukidis, I., Moumtzidou, A., Liparas, D., et al.: A hybrid graph-based and non-linear late fusion approach for multimedia retrieval. In: 2016 14th International Workshop on CBMI, pp. 1–6 (June 2016)
Acknowledgements
This work was supported by the EU’s Horizon 2020 research and innovation programme under grant agreements H2020-779962 V4Desi-gn, H2020-786731 CONNEXIONs and H2020-780656 ReTV.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Andreadis, S. et al. (2020). VERGE in VBS 2020. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_69
Download citation
DOI: https://doi.org/10.1007/978-3-030-37734-2_69
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37733-5
Online ISBN: 978-3-030-37734-2
eBook Packages: Computer ScienceComputer Science (R0)