Query by Example in Large Databases Using Key-Sample Distance Transformation and Clustering

Abstract

Calculating the similarity estimates between the query sam- ple and the database samples becomes an exhaustive task with large, usually continuously updated multimedia databases. In this paper, a fast and low complexity transformation from the original feature space into k-dimensional vector space and clustering are proposed to alleviate the problem. First k key- samples are chosen randomly from the database. These sam- ples and a distance function specify the transformation from the series of feature vectors into k-dimensional vector space where database (re)clustering can be done fast with plural- ity of traditional clustering technique whenever required. In the experiments, similarity between the samples was calcu- lated by using the Euclidean distance between their associated feature vector probability density functions. The k-means al- gorithm was used to cluster the transformed samples in the vector space. The experiments show that considerable time and computational savings are achieved while there is only a marginal drop in performance.