VERGE: A Multimodal Interactive Search Engine for Video Browsing and Retrieval

1. Schoeffmann, K.: A user-centric media retrieval competition: the video browser showdown 2012-2014. IEEE Multimedia 21(4), 8–13 (2014)

   Article  Google Scholar 

2. Apostolidis, E., Mezaris, V.: Fast shot segmentation combining global and local visual descriptors. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6583–6587 (2014)

   Google Scholar 

3. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)

   Google Scholar 

4. Su, C.-W., Liao, H.-Y.M., Tyan, H.-R., Fan, K.-C., Chen, L.-H.: A motion-tolerant dissolve detection algorithm. IEEE Trans. Multimed. 7, 1106–1113 (2005)

   Article  Google Scholar 

5. Seo, K.-D., Park, S., Jung, S.-H.: Wipe scene-change detector based on visual rhythm spectrum. IEEE Trans. Consum. Electron. 55(2), 831–838 (2009)

   Article  Google Scholar 

6. Jegou, H., Douze, M., Schmid, C., Perez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of CVPR (2010)

   Google Scholar 

7. Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 117–128 (2011)

   Article  Google Scholar 

8. Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)

   Google Scholar 

9. Markatopoulou, F., Mezaris, V., Pittaras, N., Patras, I.: Local features and a two-layer stacking architecture for semantic concept detection in video. IEEE Trans. Emerg. Topics Comput. 3(2), 193–204 (2015)

   Article  Google Scholar 

10. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv technical report (2014)

    Google Scholar 

11. Szegedy, C., et al.: Going deeper with convolutions. In: CVPR 2015 (2015). http://arxiv.org/abs/1409.4842

12. Krizhevsky, A., Ilya, S., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing System, vol. 25, pp. 1097–1105. Curran Associates, Inc., (2012)

    Google Scholar 

13. Markatopoulou, F., Mezaris, V., Patras, I.: Cascade of classifiers based on binary, non-binary and deep convolutional network descriptors for video concept detection. In: IEEE International Conference on Image Processing (ICIP 2015). IEEE, Canada (2015)

    Google Scholar 

14. Safadi B., Quénot, G.: Re-ranking by local re-scoring for video indexing and retrieval. In: 20th ACM International Conference on Information and Knowledge Management, pp. 2081–2084 (2011)

    Google Scholar 

15. Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014)

    Article  MathSciNet  Google Scholar 

16. Gialampoukidis, I., Vrochidis, S., Kompatsiaris, I.: Fast visual vocabulary construction for image retrieval using skewed-split k-d trees. In: Proceedings of the 22nd International Conference on MultiMedia Modeling (MMM 2016), Miami (2016)

    Google Scholar 

Download references