Combining multi-visual features for efficient indexing in a large image database |
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Authors: | Anne HH Ngu Quan Z Sheng Du Q Huynh Ron Lei |
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Affiliation: | (1) School of Computer Science and Engineering, The University of New South Wales, Sydney 2052 NSW, Australia; E-mail: anne@cse.unsw.edu.au , AU;(2) School of Information Technology, Murdoch University, Perth 6150 WA, Australia; E-mail: d.huynh@murdoch.edu.au , AU |
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Abstract: | The optimized distance-based access methods currently available for multidimensional indexing in multimedia databases have
been developed based on two major assumptions: a suitable distance function is known a priori and the dimensionality of the
image features is low. It is not trivial to define a distance function that best mimics human visual perception regarding
image similarity measurements. Reducing high-dimensional features in images using the popular principle component analysis
(PCA) might not always be possible due to the non-linear correlations that may be present in the feature vectors. We propose
in this paper a fast and robust hybrid method for non-linear dimensions reduction of composite image features for indexing
in large image database. This method incorporates both the PCA and non-linear neural network techniques to reduce the dimensions
of feature vectors so that an optimized access method can be applied. To incorporate human visual perception into our system,
we also conducted experiments that involved a number of subjects classifying images into different classes for neural network
training. We demonstrate that not only can our neural network system reduce the dimensions of the feature vectors, but that
the reduced dimensional feature vectors can also be mapped to an optimized access method for fast and accurate indexing.
Received 11 June 1998 / Accepted 25 July 2000 Published online: 13 February 2001 |
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Keywords: | : Image retrieval – High-dimensional indexing – Neural network |
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