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Using particle swarm optimization for image regions annotation


Sami, M and El-Bendary, N and Kim, T-H and Hassanien, AE, Using particle swarm optimization for image regions annotation, Proceedings of the 4th International Conference on Future Generation Information Technology, 16-19 December 2012, Gangneung, Kangwondo, Korea, pp. 241-250. ISBN 9783642355851 (2012) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 Springer-Verlag Berlin Heidelberg

DOI: doi:10.1007/978-3-642-35585-1_34


In this paper, we propose an automatic image annotation approach for region labeling that takes advantage of both context and semantics present in segmented images. The proposed approach is based on multi-class K-nearest neighbor, k-means and particle swarm optimization (PSO) algorithms for feature weighting, in conjunction with normalized cuts-based image segmentation technique. This hybrid approach refines the output of multi-class classification that is based on the usage of K-nearest neighbor classifier for automatically labeling images regions from different classes. Each input image is segmented using the normalized cuts segmentation algorithm then a descriptor created for each segment. The PSO algorithm is employed as a search strategy for identifying an optimal feature subset. Extensive experimental results demonstrate that the proposed approach provides an increase in accuracy of annotation performance by about 40%, via applying PSO models, compared to having no PSO models applied, for the used dataset. © 2012 Springer-Verlag.

Item Details

Item Type:Refereed Conference Paper
Keywords:image segmentation. learning (artificial intelligence), particle swarm optimisation, pattern classification, search problems
Research Division:Mathematical Sciences
Research Group:Pure mathematics
Research Field:Category theory, k theory, homological algebra
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Kim, T-H (Dr Tai Kim)
ID Code:85205
Year Published:2012
Deposited By:Research Division
Deposited On:2013-06-18
Last Modified:2015-03-03

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