Design, Implementation, and Evaluation of a Parallel Image Shape Indexer

This paper describes the design, implementation, and evaluation of a parallel indexer called PAMIS for a polygonal 2D shape image database. PAMIS is based on a shape representation scheme called the turning function, which exhibits the desirable properties of position-, scale-, and rotation-invariance, and has a similarity metric function that satisfies the triangular inequality, which is required for efficient database indexing. Because the goal of the PAMIS project is to support ”like-this” image queries, the indexing scheme we chose, the vantage-point tree (VPT), uses relative rather than absolute distance values to organize the database elements for efficient nearest-neighbor search. We have successfully implemented PAMIS on a network of workstations to exploit the I/O and computation parallelism inherent in the VPT algorithm. We found that it is preferable to make the VPT node size as small as possible in order to have a lean and deep VPT structure, and the best-case scheduling strategy performs the best among the scheduling strategies considered. Overall, the performance of the VPT algorithm scales very well with the number of processors, and the indexing efficiency, defined as the percentage of database elements touched by the search, of PAMIS is 6% and 39% for ”good” queries that ask for 1 and 50 nearest neighbors, respectively.