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Automatic image annotation with low-level features and conditional random fields
P. 197–201.
Bronevich A.G., Melnichenko A. S.
Язык:
английский
Проноза Е. В., Yagunova E., , in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2016Vol. 1. Issue 9623.: Springer Publishing Company, 2018. P. 573–587.
Добавлено: 30 октября 2020 г.
Morozov S., Бабенко А. В., , in: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2019).: IEEE, 2019. P. 3036–3045.
We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems. While the deep learning machinery has benefited literally all computer vision pipelines, the existing state-of-the-art compression methods employ shallow architectures, and we aim to close this gap by our paper. In more detail, we introduce a ...
Добавлено: 6 июля 2020 г.
Бабенко А. В., Lempitsky V., IEEE Transactions on Pattern Analysis and Machine Intelligence 2015 Vol. 37 No. 6 P. 1247–1260
A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of ...
Добавлено: 3 сентября 2015 г.
Babenko A., IEEE Transactions on Pattern Analysis and Machine Intelligence 2014 Vol. PP No. 99 P. 1
A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of ...
Добавлено: 19 декабря 2014 г.
Babenko A., Slesarev A., Chigorin A. и др., , in: Lecture Notes in Computer Science. Proceedings of the 13th European Conference on Computer Vision (ECCV 2014)* 1. Vol. 8689.: Zürich: Springer, 2014. P. 584–599.
Добавлено: 1 октября 2014 г.
Babenko A., Lempitsky V., , in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012).: Providence: IEEE, 2012. P. 3069–3076.
Добавлено: 1 октября 2014 г.
Heidelberg: Springer, 2012.
We describe a novel method for the analysis of research activities of an organization by mapping that to a taxonomy tree of the field. The method constructs fuzzy membership profiles of the organizationmembers or teams in terms of the taxonomy’s leaves (research topics), and then it generalizes them in two steps. These steps are: (i) ...
Добавлено: 29 октября 2013 г.
Automatic image annotation based on low-level features and classification of the statistical classes
Броневич А. Г., Melnichenko A. S., , in: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. ProceedingsVol. 6743.: Berlin, Heidelberg: Springer, 2011. P. 314–321.
В данной работе рассматривается проблема автоматического аннотирования изображений набором ключевых слов, что позволяет осуществлять поиск изображений в больших коллекциях по текстовому запросу. Рассматривается общая схема аннотации с использованием глобальных низкоуровневых признаков изображений, представляемых как статистические классы. С помощью процедуры классификации статистических классов, основанной на предлагаемой мере включения, производится построение вторичных информативных признаков изображений, по которым ...
Добавлено: 2 марта 2013 г.