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Content-based image retrieval over IEEE 802.11b noisy wireless networks

dc.contributor.authorChatzistavros, Evaggelos
dc.contributor.authorChatzichristofis, Savvas A.
dc.contributor.authorZagoris, Konstatinos
dc.contributor.authorStamatelos, George
dc.date.accessioned2017-10-24T12:24:46Z
dc.date.available2017-10-24T12:24:46Z
dc.date.issued2014
dc.identifier.issn1074-5351
dc.identifier.urihttp://hdl.handle.net/11728/10151
dc.description.abstractMobile devices such as smartphones and tablets are widely used in everyday life to perform a variety of operations, such as e-mail exchange, connection to social media, bank/financial transactions, and so on. Moreover, because of the large growth of multimedia applications, video and image transferring and sharing via a wireless network is becoming increasingly popular. Several modern mobile applications perform information retrieval and image recognition. For example, Google Goggles is an image recognition application that is used for searches based on pictures taken by handheld devices. In most of the cases, image recognition procedure is an image retrieval procedure. The captured images or a low-level description of them are uploaded online, and the system recognizes their content by retrieving visually similar pictures. Taking into account the last comment, our goal in this paper is to evaluate the process of image retrieval/recognition over an Institute of Electrical and Electronics Engineers 802.11b network, operating at 2.4 GHz. Our evaluation is performed through a simulated network configuration, which consists of a number of mobile nodes communicating with an access point. Throughout our simulations, we examine the impact of several factors, such as the existence of a strong line of sight during the communication between wireless devices. Strong line of sight depends on the fading model used for the simulations and has an effect on BER. We have used a large number of image descriptors and a variety of scenarios, reported in the relative literature, in order to comprehensively evaluate our system. To reinforce our results, experiments were conducted on two well-known images databases by using 10 descriptors from the literature.en_UK
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.relation.ispartofseriesInternational Journal of Communication Systems;Vol.28
dc.rights© 2014 John Wiley & Sons, Ltd.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectIEEE 802.11ben_UK
dc.subjectimage retrievalen_UK
dc.subjectBit Error Rateen_UK
dc.subjectMultimedia Applicationsen_UK
dc.subjectNoisy Environmenten_UK
dc.titleContent-based image retrieval over IEEE 802.11b noisy wireless networksen_UK
dc.typeArticleen_UK
dc.doi10.1002/dac.2724en_UK


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© 2014 John Wiley & Sons, Ltd.
Except where otherwise noted, this item's license is described as © 2014 John Wiley & Sons, Ltd.