TY - GEN
T1 - A new high-dimensional index structure using a cell-based filtering technique
AU - Han, Sung Geun
AU - Chang, Jae Woo
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.
PY - 2000
Y1 - 2000
N2 - In general, multimedia database applications require to support similarity search for content-based retrieval on multimedia data, i.e., image, animation, video, and audio. Since the similarity of two multimedia objects is measured as the distance between their feature vectors, the similarity search corresponds to a search for the nearest neighbors in the feature vector space. In this paper, we propose a new high-dimensional indexing scheme using a cell-based filtering technique which supports the nearest neighbor search efficiently. Our Cell-Based Filtering (CBF) scheme divides a high-dimensional feature vector space into cells, like VA-file. However, in order to make a better effect on filtering, our CBF scheme performs additional filtering based on a distance between an object feature vector and the center of a cell including it, in addition to filtering based on cell signatures before accessing a data file. From our experiment using high-dimensional feature vectors, we show that our CBF scheme achieves better performance on the nearest neighbor search than its competitors, such as VA-File and X-tree.
AB - In general, multimedia database applications require to support similarity search for content-based retrieval on multimedia data, i.e., image, animation, video, and audio. Since the similarity of two multimedia objects is measured as the distance between their feature vectors, the similarity search corresponds to a search for the nearest neighbors in the feature vector space. In this paper, we propose a new high-dimensional indexing scheme using a cell-based filtering technique which supports the nearest neighbor search efficiently. Our Cell-Based Filtering (CBF) scheme divides a high-dimensional feature vector space into cells, like VA-file. However, in order to make a better effect on filtering, our CBF scheme performs additional filtering based on a distance between an object feature vector and the center of a cell including it, in addition to filtering based on cell signatures before accessing a data file. From our experiment using high-dimensional feature vectors, we show that our CBF scheme achieves better performance on the nearest neighbor search than its competitors, such as VA-File and X-tree.
UR - https://www.scopus.com/pages/publications/23044520428
U2 - 10.1007/3-540-44472-6_7
DO - 10.1007/3-540-44472-6_7
M3 - Conference paper
AN - SCOPUS:23044520428
SN - 3540679774
SN - 9783540679776
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 92
BT - Current Issues in Databases and Information Systems - East-European Conference on Advances in Databases and Information Systems Held Jointly with International Conference on Database Systems for Advanced Applications, ADBIS-DASFAA 2000, Proceedings
A2 - Stuller, Julius
A2 - Pokorny, Jaroslav
A2 - Masunaga, Yoshifumi
A2 - Thalheim, Bernhard
PB - Springer Verlag
T2 - East-European Conference on Advances in Databases and Information Systems Held Jointly with International Conference on Database Systems for Advanced Applications, ADBIS-DASFAA 2000
Y2 - 5 September 2000 through 9 September 2000
ER -