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Content‐based image retrieval over IEEE 802.11b noisy wireless networks
Author(s) -
Chatzistavros Evaggelos,
Chatzichristofis Savvas A.,
Zagoris Konstatinos,
Stamatelos George
Publication year - 2014
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2724
Subject(s) - computer science , upload , mobile device , image retrieval , wireless network , wireless , variety (cybernetics) , multimedia , artificial intelligence , image (mathematics) , telecommunications , world wide web
Summary Mobile 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. Copyright © 2014 John Wiley & Sons, Ltd.