• Journal of Internet Computing and Services
• /
• v.10 no.6
• /
• pp.173-189
• /
• 2009

Recently, video data has attracted many interest. That is the reason why efficient indexing schemes are required to support the content-based retrieval of video data. But most indexing schemes are not suitable for indexing a high-dimensional data except Hybrid Spill-Tree. In this paper, we propose an efficient high-dimensional indexing scheme to support the content-based retrieval of video data. For this, we extend Hybrid Spill-Tree by using a newly designed clustering technique and by adopting a signature method. Finally, we show that proposed signature-based high dimensional indexing scheme achieves better retrieval performance than existing M-Tree and Hybrid Spill-Tree.

• Journal of KIISE:Databases
• /
• v.37 no.5
• /
• pp.228-237
• /
• 2010

Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances as well as to support index scalability for large scale data. To support these requirements, we propose a distributed high-dimensional indexing structure based on cluster systems, called a Distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and VA-files. We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate the performance of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method contributes to significant performance advantages over existing index structures on difference kinds of datasets.

• The KIPS Transactions:PartD
• /
• v.19D no.2
• /
• pp.151-160
• /
• 2012

Most of the distributed high-dimensional indexing structures provide a reasonable search performance especially when the dataset is uniformly distributed. However, in case when the dataset is clustered or skewed, the search performances gradually degrade as compared with the uniformly distributed dataset. We propose a method of improving the k-nearest neighbor search performance for the distributed vector approximation-tree based on the strongly clustered or skewed dataset. The basic idea is to compute volumes of the leaf nodes on the top-tree of a distributed vector approximation-tree and to assign different number of bits to them in order to assure an identification performance of vector approximation. In other words, it can be done by assigning more bits to the high-density clusters. We conducted experiments to compare the search performance with the distributed hybrid spill-tree and distributed vector approximation-tree by using the synthetic and real data sets. The experimental results show that our proposed scheme provides consistent results with significant performance improvements of the distributed vector approximation-tree for strongly clustered or skewed datasets.