This paper proposes a novel indexing method for complex similarity queries in high-dimensional image and video systems. In order to provide the indexing method with the flexibility in dealing with multiple features and multiple query objects, we treat every dimension independently. The efficiency of our method is realized by a specialized bitmap indexing that represents all objects in a database as a set of bitmaps. The percentage of data accessed in our indexing method is inversely proportional to the overall dimensionality, and thus the performance deterioration with the increasing dimensionality does not occur. To demonstrate the efficacy of our method we conducted extensive experiments and compared the performance with the VA-file-based index and the linear scan by using real image and video datasets, and obtained a remarkable speed-up over them.

A new indexing method for complex similarity queries in immersive multimedia systems

-With the increasing popularity of the World Wide Web comes the enormous increase in stored digital contents, which could challenge users to search and use the multimedia data efficiently. This work focuses on hastening techniques for efficient retrieval of multimedia data. In this paper, we exploit the use of bit-vectors to accelerate queries in multimedia databases. We use a compressed bit-vector to minimize the amount of data cashed on disk; thus, reducing the amount of memory and time needed to execute queries. We also compare our scheme with other related strategies. Keywords-multimedia; bit vectors; query accelerations.

Rawa Karaki

Papers

Query acceleration in multimedia database systems