Title:  ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval
Issue Date:  2013
Internal authors: 
Authors:  Tronci R; Murgia G; Pili M; Piras L; Giacinto G
Number of authors:  5
Language:  Inglese
Journal:  STUDIES IN COMPUTATIONAL INTELLIGENCE
Volume:  439
First page:  53
Last page:  70
Number of pages:  18
Digital Object Identifier (DOI):  http://dx.doi.org/10.1007/978-3-642-31546-6_4
Scopus identifier:  2-s2.0-84867949227
Book title:  New Challenges in Distributed Information Filtering and Retrieval
URL:  http://link.springer.com/chapter/10.1007%2F978-3-642-31546-6_4#
ISBN:  978-3-642-31545-9
Abstract:  Nowadays, a very large number of digital image archives is easily produced thanks to the wide diffusion of personal digital cameras and mobile devices with embedded cameras. Thus, personal computers, personal storage units, as well as photo-sharing and social-network websites, are rapidly becoming the repository for thousands, or even billions of images (i.e., more than 100 million photos are uploaded every day on the social site Facebook). As a consequence, there is an increasing need for tools enabling the semantic search, classification, and retrieval of images. The use of meta-data associated to images solves the problems only partially, as the process of assigning reliable meta-data to images is not trivial, is slow, and closely related to whom performed the task. One solution for effective image search and retrieval is to combine content-based analysis with feedbacks from the users. In this chapter we present Image Hunter, a tool that implements a Content Based Image Retrieval (CBIR) engine with a Relevance Feedback mechanism. Thanks to a user friendly interface the tool is especially suited to unskilled users. In addition, the modular structure permits the use of the same core both in web-based and stand alone applications.
Peer review:  Sì, ma tipo non specificato
scientifica
Type: 2.1 Contributo in volume (Capitolo o Saggio)

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