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Unsupervised Classification of Images: A Review
Abass Olaode, Golshah Naghdy, Catherine Todd
Pages - 325 - 342     |    Revised - 10-08-2014     |    Published - 15-09-2014
Volume - 8   Issue - 5    |    Publication Date - September / October 2014  Table of Contents
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KEYWORDS
Image Retrieval, Image Categorisation, Unsupervised Learning, Clustering, Dimension Reduction.
ABSTRACT
Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples. Unsupervised categorisation of images relies on unsupervised machine learning algorithms for its implementation. This paper identifies clustering algorithms and dimension reduction algorithms as the two main classes of unsupervised machine learning algorithms needed in unsupervised image categorisation, and then reviews how these algorithms are used in some notable implementation of unsupervised image classification algorithms.
CITED BY (1)  
1 Olaode, A. A., Naghdy, G., & Todd, C. A. Bag-of-Visual Words Codebook Development for the Semantic Content Based Annotation of Images.
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Mr. Abass Olaode
University of Wollongong - Australia
aao808@uowmail.edu.au
Mr. Golshah Naghdy
School of Electrical Computer Telecommunication Engineering University of Wollongong Wollongong, 2500, Australia - Australia
Dr. Catherine Todd
School of Electrical Computer Telecommunication Engineering University of Wollongong Dubai, UAE - United Arab Emirates