Elenco Pubblicazioni - Publications (1524)
R. Connor, L. Vadicamo, F. A. Cardillo, F. Rabitti: “Supermetric Search with the Four-Point Proper-ty”, Proceedings of the 9th International Conference on Similiarty Search and Applications (SISAP 2016). Springer International Publishing. Oct 2016
Written by LUCIA VADICAMO
Metric indexing research is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as…
G. Amato, F. Falchi, F. Rabitti, and L. Vadicamo: “Combining Fisher Vector and Convolutional Neural Networks for image retrieval”, In Proceedings 7th Italian Information Retrieval Workshop (IIR 2016), CEUR Workshop Proceedings, May 2016
Written by LUCIA VADICAMO
Abstract. Fisher Vector (FV) and deep Convolutional Neural Network (CNN) are two popular approaches for extracting e ective image representations. FV aggregates local information (e.g., SIFT) and have been state-of-the-art before the recent success of…
G. Amato,P. Bolettieri,F. Falchi,C. Gennaro,L. Vadicamo:"Using Apache Lucene to Search Vector of Locally Aggregated Descriptors",Proceedings of the 11th International Conference on Computer Vision Theory and Applications- Volume 4: VISAPP, Feb 2016
Written by LUCIA VADICAMO
Abstract: Surrogate Text Representation (STR) is a profitable solution to efficient similarity search on metric space using conventional text search engines, such as Apache Lucene. This technique is based on comparing the permutations of some…
G. Amato, F. Falchi, L. Vadicamo: "How Effective Are Aggregation Methods on Binary Features?", In: Proceedings of the 11th International Conference on Computer Vision Theory and Applications- Volume 4: VISAPP 2016, pages 566-573, February 2016
Written by LUCIA VADICAMO
During the last decade, various local features have been proposed and used to support Content Based Image Retrieval and object recognition tasks. Local features allow to effectively match local structures between images, but the cost…
R. Connor, F. A. Cardillo, L. Vadicamo, F. Rabitti: "Hilbert Exclusion: Improved Metric Search through Finite Isometric Embeddings", ACM Transactions on Information Systems (TOIS), 35(3), 17, June 2017
Written by LUCIA VADICAMO
Most research into similarity search in metric spaces relies upon the triangle inequality property. This property allows the space to be arranged according to relative distances to avoid searching some subspaces. We show that many…
