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...

• Type: Conference
• Year: 2001
• Where: VLDB
• Authors: Anne H. H. Ngu, Quan Z. Sheng, Du Q. Huynh, Ron Lei
• Keywords: Database, Feature Vectors, Non-linear Neural Network, Optimized Access Method, VLDB 2001