NDLI: Incorporating random forest trees with particle swarm optimization for automatic image annotation

Content Provider	IEEE Xplore Digital Library
Author	Sami, M. Hassanien, A.E. El-Bendary, N. Berwick, R.C.
Copyright Year	2012
Description	Author affiliation: Faculty of Computers and Information - Cairo University, Scientific Research Group in Egypt (SRGE), Cairo - Egypt (Sami, M.; Hassanien, A.E.) \|\| Arab Academy for Science, Technology, and Maritime Transport, Scientific Research Group in Egypt (SRGE), Cairo - Egypt (El-Bendary, N.) \|\| Massachusetts Institute of Technology (MIT), USA (Berwick, R.C.)
Abstract	This paper presents an automatic image annotation approach that integrates the random forest classifier with particle swarm optimization algorithm for classes' scores weighting. The proposed hybrid approach refines the output of multi-class classification that is based on the usage of random forest classifier for automatically labeling images with a number of words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. Images feature vectors are clustered into K clusters and a random forest classifier is trained for each cluster. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal weighting for classes' scores from random forest classifiers. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance of other approaches, considering annotation accuracy, for the experimented dataset.
Starting Page	763
Ending Page	769
File Size	660927
Page Count	7
File Format	PDF
ISBN	9781467307086
e-ISBN	9788360810484
e-ISBN	9788360810514
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-09
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Polish Info Processing Socit
Subject Keyword	Training Correlation Clustering algorithms Vegetation Vectors Decision trees Particle swarm optimization
Content Type	Text
Resource Type	Article

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