Parallel abstraction of images with applications in image retrieval

Image abstraction has wide spectrum of application, especially for computationally intensive application such as content-based image retrieval (CBIR). A recently developed abstraction system was demonstrated to enhance the performance of CBIR systems while reducing the space and time complexities. The system is based on Singular Value Decomposition (SVD), a tool borrowed from linear algebra. However, pre-abstracting images also requires high computational power, and has to be optimized for parallel environments in order to be practical. In this work we aim at optimizing the abstraction and creating a parallel engine for it. The new parallel system is demonstrated to run in less than one tenth the time required for the original system.