Using Apache Lucene to Search Vector of Locally Aggregated Descriptors

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 reference objects in place of the original metric distance. However, the Achilles heel of STR approach is the need to reorder the result set of the search according to the metric distance. This forces to use a support database to store the original objects, which requires efficient random I/O on a fast secondary memory (such as flash-based storages). In this paper, we propose to extend the Surrogate Text Representation to specifically address a class of visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD). This approach is based on representing the individual sub-vectors forming the VLAD vector with the STR, providing a finer representation of the vector and enabling us to get rid of the reordering phase. The experiments on a publicly available dataset show that the extended STR outperforms the baseline STR achieving satisfactory performance near to the one obtained with the original VLAD vectors.Comment: In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, p. 383-39

Sistema di riconoscimento delle immagini e mobile app

In questo articolo presentiamo un sistema di riconoscimento visuale di iscrizioni latine e greche, realizzato durante il progetto europeo EAGLE, Europeana network of Ancient Greek and Latin Epigraphy. Il sistema permette di inquadrare un'epigrafe con la camera di uno smartphone/tablet (in un museo, in un sito archeologico, per strada), e ricevere informazioni culturali associate ad essa. Gli esperimenti hanno dimostrato che la tecnica VLAD (Vector of Locally Aggregated Descriptors) è molto promettente in questo contesto.In this paper, we present a system for visually retrieving ancient inscriptions, developed in the context of the ongoing Europeana network of Ancient Greek and Latin Epigraphy (EAGLE) EU Project. The system allows the user in front of an inscription (e.g, in a museum, street, archaeological site) or watching a reproduction (e.g., in a book, from a monitor), to automatically recognize the inscription and obtain information about it just using a smart-phone or a tablet. The experimental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context

Visual Recognition in the EAGLE Project

Abstract. In this paper, we present a system for visually retrieving ancient inscriptions, developed in the context of the ongoing Europeana network of Ancient Greek and Latin Epigraphy (EAGLE) EU Project. The system allows the user in front of an inscription (e.g, in a museum, street, archaeological site) or watching a reproduction (e.g., in a book, from a monitor), to automatically recognize the inscription and obtain information about it just using a smart-phone or a tablet. The experimental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context

Visual recognition in the EAGLE Project

In this paper, we present a system for visually retrieving an- cient inscriptions, developed in the context of the ongoing Europeana network of Ancient Greek and Latin Epigraphy (EAGLE) EU Project. The system allows the user in front of an inscription (e.g, in a museum, street, archaeological site) or watching a reproduction (e.g., in a book, from a monitor), to automatically recognize the inscription and obtain information about it just using a smart-phone or a tablet. The experi- mental results show that the Vector of Locally Aggregated Descriptors is a promising encoding strategy for performing visual recognition in this specific context