Dimensionality reduction of tree structured wavelet transform texture features for content based image retrieval

Dimensionality reduction methods are of interest in applications such as content-based image and video retrieval. The focus of this paper is on the dimensionality reduction of feature vectors of tree structured wavelet decomposition for improving the retrieval speed. We have investigated a novel idea of reduction of feature dimension by concatenating the inter scale approximate coefficients and intra scale detail coefficient of each individual channel. The results are quite impressive; in an experiment using Brodatz texture database, feature vector length is reduced by a factor of three, which doubles retrieval speed without significantly reducing retrieval performance.