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Object-based Image Ranking using Neural Networks

Karmakar, Gour C.; Rahman, Syed M. and Dooley, Laurence S. (2001). Object-based Image Ranking using Neural Networks. In: ed. Proceedings of the International Conference on Computer Science (ICCS '01). Lecture Notes in Computer Science (LNCS 2). Springer-Verlag, pp. 281–290.

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In this paper an object-based image ranking is performed using both supervised and unsupervised neural networks. The features are extracted based on the moment invariants, the run length, and a composite method. This paper also introduces a likeness parameter, namely a similarity measure using the weights of the neural networks. The experimental results show that the performance of image retrieval depends on the method of feature extraction, types of learning, the values of the parameters of the neural networks, and the databases including query set. The best performance is achieved using supervised neural networks for internal query set.

Item Type: Book Section
ISBN: 3-540-42233-1, 978-3-540-42233-4
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 11491
Depositing User: Laurence Dooley
Date Deposited: 28 Aug 2008 03:39
Last Modified: 08 Dec 2018 16:19
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