Search Algorithms for Subdatatype-Based Multimedia Retrieval

Recently, the researchers mostly are interested in only the search for the data content that are globally similar to the query, not the search inside data items. This paper presents an algorithm, called \generalized virtual node (GVN)" algorithm, to search for data items where parts of them (sub-datatype) are similar to the incoming query. We call this \sub-datatype"-based multimedia retrieval. Each multimedia datatype, such as image and audio, in this paper is represented as a k-dimensional signal in the spatio-temporal domain. A k-dimensional signal is transformed into characteristic features and these features are stored in a hierarchical multidimensional structure, called the \k-tree." Each node on the k-tree contains partial content corresponding to the spatial and/or temporal positions in the data. The k-tree structure allows us to build a uni ed retrieval model for any types of multimedia data. It also eliminates unnecessary comparisons of cross-media querying. The experimental results of the use of the new GVN algorithm for \subaudio" and \sub-image" retrievals show that it takes much less retrieval times than other earlier algorithms, such as brute-force and Partial-matching algorithm, while the accuracy is acceptable.