A parallel algorithm for multi-feature content-based multimedia retrieval

摘要

In this paper, we propose a parallel, unified model for the indexing and retrieval by content of multimedia data using a weighted cascade. [4] Each multimedia data type can be viewed as k-dimensional (k-d) data in spatio-temporal domain. [3] Each dimension of the data is separated into small blocks and then formed into a multidimensional tree structure, called a k-tree. Using the k-tree structure, the retrieval time improves while the retrieval accuracy remains relatively constant. Moreover, since we can realize all types of multimedia data using the same k-tree data structure, the data indexing and retrieval algorithms are uniform. In this paper, we demonstrate a parallel algorithm with a weighted cascade that can be used with the uniform model. The parallel processing and weighted cascade algorithm improve the retrieval time. We evaluated the performance results of multimedia database queries using a Beowulf-class cluster of workstations. [1] The experimental results indicate that the retrieval accuracy improves with the utilized tree depth, while the parallel processing minimizes the retrieval time.