In-picture search algorithm for content-based image retrieval

Researchers are currently more interested in searching for fragments that are similar to a query, than a total data item that is similar to a query; the search interest is for “contains”, not “is”. This paper presents an O(logn) algorithm, called the “generalized virtual node (GVN)” algorithm; the GVN algorithm is a search algorithm for data fragments that have similar contents to that of a query. An example of the use of the GVN algorithm is in the search of an image database for a certain picture object regardless to what their picture backgrounds are. Each image is transformed into characteristic features and these features are stored in a hierarchical multidimensional structure, called a “k-tree.” The k-tree is exploited to build a unified retrieval model for any type of multimedia data. The experimental results of this “in-picture” search algorithm on an image database demonstrate a search quality that is qualitatively and quantitatively acceptable, with a retrieval time that is faster than other algorithms, such as brute-force and partial matching