The Open UniversitySkip to content
 

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.

Full text available as:
[img]
Preview
PDF (Not Set) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (221Kb)
URL: http://www.springerlink.com/content/9hdg1atg1kwyw1...
DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1007/3-540-45718-6_32
Google Scholar: Look up in Google Scholar

Abstract

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 Chapter
ISBN: 3-540-42233-1, 978-3-540-42233-4
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 11491
Depositing User: Laurence Dooley
Date Deposited: 28 Aug 2008 03:39
Last Modified: 25 Jan 2011 16:03
URI: http://oro.open.ac.uk/id/eprint/11491
Share this page:

Altmetrics

Scopus Citations

Actions (login may be required)

View Item
Report issue / request change

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk