Research/implementation Project Proposal: Efficiency Analysis of Quasi-random Sampling Algorithm for Contents-based Image Retrieval

Recently, Zhou and Robles-Kelly proposed a novel quasi-random sampling (QuaRS) approach for CBIR [4]. This approach uses EM algorithm to organize the images in the database into compact clusters, then compares the similarity between the query and the clustered images to govern the sampling process within clusters. The sampling can be viewed as a stratified sampling one which is random at the cluster level and takes into account the intra-cluster structure of the dataset. This approach leads to a measure of statistical confidence that relates to the theoretical hard-limit of the retrieval performance. Although this approach is expected to be efficient when applied to vary large database, there is no theoretical and experimental analysis of the efficiency of this sampling algorithm.