Abstract
The German Broadcasting Archive (DRA) maintains the cultural heritage of radio and television broadcasts of the former German Democratic Republic (GDR). The uniqueness and importance of the video material stimulates a large scientific interest in the video content. In this paper, we present an automatic video analysis and retrieval system for searching in historical collections of GDR television recordings. It consists of video analysis algorithms for shot boundary detection, concept classification, person recognition, text recognition and similarity search. The performance of the system is evaluated from a technical and an archival perspective on 2,500 h of GDR television recordings.
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References
Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Albertson, D., Ju, B.: Design criteria for video digital libraries: categories of important features emerging from users’ responses. Online Inf. Rev. 39(2), 214–228 (2015)
Belhumeur, P.N., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Breuel, T.M., Ul-Hasan, A., Al-Azawi, M.A., Shafait, F.: High-performance OCR for printed English and Fraktur using LSTM networks. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 683–687 (2013)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: Proceedings of the British Machine Vision Conference, pp. 1–11 (2014)
Christel, M., Kanade, T., Mauldin, M., Reddy, R., Sirbu, M., Stevens, S.M., Wactlar, H.D.: Informedia digital video library. Commun. ACM 38(4), 57–58 (1995)
Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 48:1–48:9 (2009)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 2–9 (2009)
Ewerth, R., Freisleben, B.: Video cut detection without thresholds. In: Proceedings of the 11th International Workshop on Signals, Systems and Image Processing (IWSSIP 2004), Poznan, Poland, pp. 227–230 (2004)
Gong, Y., Jia, Y., Leung, T., Toshev, A., Ioffe, S.: Deep convolutional ranking for multilabel image annotation. arXiv preprint arXiv:1312.4894 (2013)
Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), pp. 6645–6649 (2013)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678 (2014)
Krizhevsky, A., Hinton, G.: Using very deep autoencoders for content-based image retrieval. In: Proceedings of the European Symposium on Artificial Neural Networks, pp. 1–7 (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1–9 (2012)
Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 27–35 (2015)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Marchionini, G., Geisler, G.: The open video digital library. D-Lib Mag. 8(12), 1082–9873 (2002)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)
Mühling, M.: Visual concept detection in images and videos. Ph.D. thesis, University of Marburg (2014)
Mühling, M., Ewerth, R., Zhou, J., Freisleben, B.: Multimodal video concept detection via bag of auditory words and multiple kernel learning. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 40–50. Springer, Heidelberg (2012)
Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approximate Reasoning 50(7), 969–978 (2009)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2014)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
Wan, J., Wang, D., Hoi, S.C.H., Wu, P.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the ACM International Conference on Multimedia (MM), pp. 157–166 (2014)
Yianilos, P.N.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 311–321 (1993)
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. Adv. Neural Inf. Process. Syst. 27, 487–495 (2014)
Acknowledgements
This work is financially supported by the German Research Foundation (DFG-Programm “Förderung herausragender Forschungsbibliotheken”, “Bild- und Szenenrecherche in historischen Beständen des DDR-Fernsehens im Deutschen Rundfunkarchiv durch automatische inhaltsbasierte Videoanalyse”; CR 456/1-1, EW 134/1-1, FR 791/12-1).
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Mühling, M. et al. (2016). Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2016. Lecture Notes in Computer Science(), vol 9819. Springer, Cham. https://doi.org/10.1007/978-3-319-43997-6_6
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DOI: https://doi.org/10.1007/978-3-319-43997-6_6
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