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VLDB
2001
ACM

Combining multi-visual features for efficient indexing in a large image database

9 years 4 months ago
Combining multi-visual features for efficient indexing in a large image database
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 sys...
Anne H. H. Ngu, Quan Z. Sheng, Du Q. Huynh, Ron Le
Added 05 Dec 2009
Updated 05 Dec 2009
Type Conference
Year 2001
Where VLDB
Authors Anne H. H. Ngu, Quan Z. Sheng, Du Q. Huynh, Ron Lei
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