1. Field of the Invention
The present disclosure relates to a content-based searching method and system for multimedia objects using high-dimensional feature vector data, which are capable of quickly and exactly processing a query with a 2-level signature.
2. Description of the Related Art
Due to developments of computing and media technologies, data are represented in a type of a multimedia including texts, images, audios, and videos. A searching efficiency is a major issue in handling such multimedia information. That is, the major issue is how quickly and exactly a user can search multimedia data containing desired information. A content-based searching method using an extracted high-dimensional feature vector data is generally used for searching data from multimedia objects, such as images, audios, and videos.
In the content-based searching method for such multimedia objects, there are two representative query types, that is, a range query and a k-nearest neighbor query.
The range query is a query for finding multimedia objects satisfying a similarity with a certain range, and the k-nearest neighbor query is a query for finding the k number of objects with a high similarity. In these query, the calculation of the similarity is generally achieved by a method for calculating Euclidean distance.
In order to ensure the fast searching of high-dimensional data, it is important to reduce the calculation of the similarity and the data read. To this end, high-dimensional data indexing schemes have been used, which may be proposed by being largely classified into a tree-based indexing scheme and a filtering-based indexing scheme.
In order to efficiently search objects distributed in a data space, the tree-based indexing scheme (e.g., R-Tree, X-Tree, SR-Tree, M-Tree, etc.) uses rectangles or circles indicating a group of neighbor objects as a searching unit. However, since an overlapping area between the rectangles or the circles, which indicate the group of the neighbor objects, is expanded as data dimension increase, there is raised the curse of dimension problem that the searching performance may be lower than that of a sequential searching by exponentially degrading. Therefore, there is a need for a method and system that can solve the curse of dimension problem.
The filtering-based indexing scheme (e.g., VA-File, CBF, etc.) is to improve the searching performance for high-dimensional data by using signatures and feature vectors, which is a method that sequentially reads all signature files to primarily filter them and thereafter reads the feature vectors.
The filtering-based indexing scheme has a disadvantage that accuracy is lowered when decreasing the bit size of the signature, and that data intended to be read increase when increasing the bit size of the signature.