By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed court cases of the 14th eu convention on laptop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers awarded have been conscientiously reviewed and chosen from 1480 submissions. The papers conceal all points of computing device imaginative and prescient and trend acceptance corresponding to 3D computing device imaginative and prescient; computational images, sensing and exhibit; face and gesture; low-level imaginative and prescient and picture processing; movement and monitoring; optimization tools; physics-based imaginative and prescient, photometry and shape-from-X; attractiveness: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical equipment and studying; video: occasions, actions and surveillance; functions. they're equipped in topical sections on detection, acceptance and retrieval; scene knowing; optimization; snapshot and video processing; studying; motion, task and monitoring; 3D; and nine poster sessions.
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Additional info for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I
In: ICMR (2015) 12. : All about VLAD. In: CVPR (2013) 13. : Orientation covariant aggregation of local descriptors with embeddings. , Tuytelaars, T. ) ECCV 2014. LNCS, vol. 8694, pp. 382–397. Springer, Heidelberg (2014). doi:10. 1007/978-3-319-10599-4 25 14. : Neural codes for image retrieval. , Tuytelaars, T. ) ECCV 2014. LNCS, vol. 8689, pp. 584–599. Springer, Heidelberg (2014). html 18 F. Radenovi´c et al. 15. : A baseline for visual instance retrieval with deep convolutional networks. 6574 (2014) 16.
Negative evidences and co-occurences in image retrieval: the beneﬁt of PCA and whitening. , Schmid, C. ) ECCV 2012. LNCS, vol. 7573, pp. 774–787. Springer, Heidelberg (2012). 1007/978-3-642-33709-3 55 42. : Very deep convolutional networks for large-scale image recognition. 1556 (2014) 43. : Good practice in CNN feature transfer. 00133 (2016) 44. : Fracking deep convolutional image descriptors. 6537 (2014) 45. : Modeling local and global deformations in deep learning: epitomic convolution, multiple instance learning, and sliding window detection.
It is better to train with all 3D models due to the higher variability in the training set. Remarkably, signiﬁcant increase in performance is achieved even with 10 or 100 models. However, the network is able to over-ﬁt in the case of few clusters. All models are utilized in all other experiments. Learned projections. The PCA-whitening  (PCAw ) is shown to be essential in some cases of CNN-based descriptors [14,23,25]. On the other hand, it is shown that on some of the datasets, the performance after PCAw substantially drops compared with the raw descriptors (max pooling on Oxford5k ).