dc.contributor.author | Tsochatzidis, Lazaros T. | |
dc.contributor.author | Iakovidou, Chrysanthi | |
dc.contributor.author | Chatzichristofis, Savvas A. | |
dc.contributor.author | Boutalis, Yiannis | |
dc.date.accessioned | 2017-11-02T11:16:34Z | |
dc.date.available | 2017-11-02T11:16:34Z | |
dc.date.issued | 2013 | |
dc.identifier.isbn | 978-1-4503-2404-5 | |
dc.identifier.uri | http://hdl.handle.net/11728/10212 | |
dc.description.abstract | Golden Retriever Image Retrieval Engine (GRire) is an open
source light weight Java library developed for Content Based
Image Retrieval (CBIR) tasks, employing the Bag of Visual
Words (BOVW) model. It provides a complete framework
for creating CBIR system including image analysis tools,
classi ers, weighting schemes etc., for e cient indexing and
retrieval procedures. Its eminent feature is its extensibility,
achieved through the open source nature of the library as
well as a user-friendly embedded plug-in system.
GRire is available on-line along with install and develop-
ment documentation on http://www.grire.net and on its
Google Code page http://code.google.com/p/grire. It is
distributed either as a Java library or as a standalone Java
application, both GPL licensed. | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | ACM | en_UK |
dc.relation.ispartofseries | Proceedings of the 21st ACM international conference on Multimedia;MM’13, October 21–25, 2013, Barcelona, Spain | |
dc.rights | Copyright 2013 ACM | en_UK |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en_UK |
dc.subject | Research Subject Categories::TECHNOLOGY | en_UK |
dc.subject | Image Retrieval | en_UK |
dc.subject | Visual Words | en_UK |
dc.subject | Bag-Of-Visual-Words | en_UK |
dc.subject | Open Source | en_UK |
dc.subject | Image Search | en_UK |
dc.subject | Image Indexing | en_UK |
dc.title | Golden Retriever - A Java Based Open Source Image Retrieval Engine | en_UK |
dc.title.alternative | SUBMITTED TO ACM MULTIMEDIA 2013 OPEN SOURCE SOFTWARE COMPETITION | en_UK |
dc.type | Working Paper | en_UK |
dc.doi | 10.1145/2502081.2502227 | en_UK |