Effective data co-reduction for multimedia similarity search

Multimedia similarity search has been playing a critical role in many novel applications. Typically, multimedia objects are described by high-dimensional feature vectors (or points) which are organized in databases for retrieval. Although many high-dimensional indexing methods have been proposed to facilitate the search process, efficient retrieval over large, sparse and extremely high-dimensional databases remains challenging due to the continuous increases in data size and feature dimensionality. In this paper, we propose the first framework for Data Co-Reduction (DCR) on both data size and feature dimensionality. By utilizing recently developed co-clustering methods, DCR simultaneously reduces both size and dimensionality of the original data into a compact subspace, where lower bounds of the actual distances in the original space can be efficiently established to achieve fast and lossless similarity search in the filter-andrefine approach. Particularly, DCR considers the dualit...

• Type: Journal
• Year: 2011
• Where: SIGMOD
• Authors: Zi Huang, Heng Tao Shen, Jiajun Liu, Xiaofang Zhou
• Keywords: Database, Dimensional Databases, Feature Vectors, SIGMOD 2011, Similarity Search