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Search algorithms for subdatatype-based multimedia retrieval
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DOI：10.1023/A:1008150831513.png)
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摘要
En 中文
Recently, researchers have mainly been interested only in the search for data content that are globally similar to the query and not in the search for inside data items. This paper presents an algorithm, called a generalized virtual node (GVN) algorithm, to search for data items where parts (subdatatype) are similar to the incoming query. We call this subdatatype-based multimedia retrieval. Each multimedia datatype, such as image and audio is represented in this paper 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 unified 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 subimage retrievals show that it takes much less retrieval times than other earlier algorithms such as brute-force and the partial-matching algorithm, while the accuracy is acceptable.
Keyword:
subdatatype
content-based retrieval
multimedia
unified model
search algorithms
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