Multimedia Data Indexing
Multimedia (MM) data indexing refers to the problem of preprocessing a database of MM objects so that they can be efficiently searched for on the basis of their content. Due to the nature of MM data, indexing solutions are needed to efficiently support similarity queries, where the similarity of two objects is usually defined by some expert of the domain and can vary depending on the specific application. Peculiar features of MM indexing are the intrinsic high-dimensional nature of the data to be organized and the complexity of similarity criteria that are used to compare objects. Both aspects are therefore to be considered for designing efficient indexing solutions.
Earlier approaches to the problem of MM data indexing date back to the beginning of 1990s, when it became apparent the need of efficiently supporting queries on large collections of nonstandard data types, such as images and time series. Representing the content of such...
- 5.Ciaccia P, Patella M, Zezula P. M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of the 23rd International Conference on Very Large Data Bases; 2007. p. 426–35.Google Scholar
- 7.Goyal N, Lifshits Y, Schütse H. Disorder inequality: a combinatorial approach to nearest neighbor search. In: Proceedings of the 1st ACM International Conference on Web Search and Data Mining; 2008. p. 25–32.Google Scholar
- 8.Jagadish HV. A retrieval technique for similar shapes. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1991. p. 208–17.Google Scholar
- 10.Lee J, Oh JH, Hwang S. STRG-index: spatio-temporal region graph indexing for large video databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2005. p. 718–29.Google Scholar
- 11.Skopal T. On fast non-metric similarity search by metric access methods. In: Advances in Database Technology, Proceedings of the 10th International Conference on Extending Database Technology; 2006. p. 718–36.Google Scholar
- 12.Skopal T, Hoksza D. Improving the performance of M-tree family by nearest-neighbor graphs. In: Proceedings of the 11th East European Conference Advances in Databases and Information Systems; 2007. p. 172–88.Google Scholar
- 13.Vlachos M, Vagena Z, Yu PS, Athitsos V. Rotation invariant indexing of shapes and line drawings. In: Proceedings of the ACM International Conference on Information and Knowledge Management; 2005. p. 131–8.Google Scholar